Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for subject:(Generative Adversarial Networks). Showing records 1 – 30 of 56 total matches.

[1] [2]

Search Limiters

Last 2 Years | English Only

Levels

Country

▼ Search Limiters


University of Technology, Sydney

1. Wang, Chaoyue. Generative modelling and adversarial learning.

Degree: 2018, University of Technology, Sydney

 A main goal of statistics and machine learning is to represent and manipulate high-dimensional probability distributions of real-world data, such as natural images. Generative adversarial(more)

Subjects/Keywords: Generative modelling.; Adversarial learning.; Generative adversarial networks.; Perceptual adversarial loss.

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wang, C. (2018). Generative modelling and adversarial learning. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/127910

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wang, Chaoyue. “Generative modelling and adversarial learning.” 2018. Thesis, University of Technology, Sydney. Accessed December 06, 2019. http://hdl.handle.net/10453/127910.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wang, Chaoyue. “Generative modelling and adversarial learning.” 2018. Web. 06 Dec 2019.

Vancouver:

Wang C. Generative modelling and adversarial learning. [Internet] [Thesis]. University of Technology, Sydney; 2018. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/10453/127910.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang C. Generative modelling and adversarial learning. [Thesis]. University of Technology, Sydney; 2018. Available from: http://hdl.handle.net/10453/127910

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

2. -5777-2824. A study of generative adversarial networks and possible extensions of GANs.

Degree: MSin Engineering, Electrical and Computer Engineering, 2017, University of Texas – Austin

 The goal of our research is to explore the power of generative adversarial networks (GANs). We take a review of deep learning and many extended… (more)

Subjects/Keywords: Deep learning; Generative adversarial networks

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

-5777-2824. (2017). A study of generative adversarial networks and possible extensions of GANs. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/61664

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-5777-2824. “A study of generative adversarial networks and possible extensions of GANs.” 2017. Masters Thesis, University of Texas – Austin. Accessed December 06, 2019. http://hdl.handle.net/2152/61664.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-5777-2824. “A study of generative adversarial networks and possible extensions of GANs.” 2017. Web. 06 Dec 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-5777-2824. A study of generative adversarial networks and possible extensions of GANs. [Internet] [Masters thesis]. University of Texas – Austin; 2017. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/2152/61664.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-5777-2824. A study of generative adversarial networks and possible extensions of GANs. [Masters Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/61664

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


UCLA

3. Liu, Yingzhu. Face Aging Using Deep Convolutional Generative Adversarial Network with Condition.

Degree: Statistics, 2019, UCLA

 We explore multiple ideas on face aging, and we finally settle down on constructing a Face Reconstruction Convolutional Neural Network and a Feature Vector Encoder.… (more)

Subjects/Keywords: Statistics; Convolutional Neural Networks; Generative Adversarial Networks

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Liu, Y. (2019). Face Aging Using Deep Convolutional Generative Adversarial Network with Condition. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/88j0p2nx

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Liu, Yingzhu. “Face Aging Using Deep Convolutional Generative Adversarial Network with Condition.” 2019. Thesis, UCLA. Accessed December 06, 2019. http://www.escholarship.org/uc/item/88j0p2nx.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Liu, Yingzhu. “Face Aging Using Deep Convolutional Generative Adversarial Network with Condition.” 2019. Web. 06 Dec 2019.

Vancouver:

Liu Y. Face Aging Using Deep Convolutional Generative Adversarial Network with Condition. [Internet] [Thesis]. UCLA; 2019. [cited 2019 Dec 06]. Available from: http://www.escholarship.org/uc/item/88j0p2nx.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liu Y. Face Aging Using Deep Convolutional Generative Adversarial Network with Condition. [Thesis]. UCLA; 2019. Available from: http://www.escholarship.org/uc/item/88j0p2nx

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Rochester Institute of Technology

4. Ramesh, Chandini. A Comparative Assessment of the Impact of Various Norms on Wasserstein Generative Adversarial Networks.

Degree: MS, Computer Science (GCCIS), 2019, Rochester Institute of Technology

Generative Adversarial Networks (GANs) provide a fascinating new paradigm in machine learning and artificial intelligence, especially in the context of unsupervised learning. GANs are… (more)

Subjects/Keywords: Generative adversarial networks; LASSO regression; Norms; Ridge regression; Wasserstein distance; Wasserstein generative adversarial networks

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ramesh, C. (2019). A Comparative Assessment of the Impact of Various Norms on Wasserstein Generative Adversarial Networks. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9989

Chicago Manual of Style (16th Edition):

Ramesh, Chandini. “A Comparative Assessment of the Impact of Various Norms on Wasserstein Generative Adversarial Networks.” 2019. Masters Thesis, Rochester Institute of Technology. Accessed December 06, 2019. https://scholarworks.rit.edu/theses/9989.

MLA Handbook (7th Edition):

Ramesh, Chandini. “A Comparative Assessment of the Impact of Various Norms on Wasserstein Generative Adversarial Networks.” 2019. Web. 06 Dec 2019.

Vancouver:

Ramesh C. A Comparative Assessment of the Impact of Various Norms on Wasserstein Generative Adversarial Networks. [Internet] [Masters thesis]. Rochester Institute of Technology; 2019. [cited 2019 Dec 06]. Available from: https://scholarworks.rit.edu/theses/9989.

Council of Science Editors:

Ramesh C. A Comparative Assessment of the Impact of Various Norms on Wasserstein Generative Adversarial Networks. [Masters Thesis]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/9989


University of Illinois – Urbana-Champaign

5. Chen, Yucheng. Deep generative models via explicit Wasserstein minimization.

Degree: MS, Computer Science, 2019, University of Illinois – Urbana-Champaign

 This thesis provides a procedure to fit generative networks to target distributions, with the goal of a small Wasserstein distance (or other optimal transport costs).… (more)

Subjects/Keywords: Deep Generative Models; Generative Adversarial Networks; Optimal Transport

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Chen, Y. (2019). Deep generative models via explicit Wasserstein minimization. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/104932

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Chen, Yucheng. “Deep generative models via explicit Wasserstein minimization.” 2019. Thesis, University of Illinois – Urbana-Champaign. Accessed December 06, 2019. http://hdl.handle.net/2142/104932.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Chen, Yucheng. “Deep generative models via explicit Wasserstein minimization.” 2019. Web. 06 Dec 2019.

Vancouver:

Chen Y. Deep generative models via explicit Wasserstein minimization. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2019. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/2142/104932.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen Y. Deep generative models via explicit Wasserstein minimization. [Thesis]. University of Illinois – Urbana-Champaign; 2019. Available from: http://hdl.handle.net/2142/104932

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


San Jose State University

6. Deshmukh, Kunal Rajan. Image Compression Using Neural Networks.

Degree: MS, Computer Science, 2019, San Jose State University

  Image compression is a well-studied field of Computer Vision. Recently, many neural network based architectures have been proposed for image compression as well as… (more)

Subjects/Keywords: Convolutional Neural Networks; Generative Adversarial Networks; Artificial Intelligence and Robotics

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Deshmukh, K. R. (2019). Image Compression Using Neural Networks. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.h8mt-65ct ; https://scholarworks.sjsu.edu/etd_projects/666

Chicago Manual of Style (16th Edition):

Deshmukh, Kunal Rajan. “Image Compression Using Neural Networks.” 2019. Masters Thesis, San Jose State University. Accessed December 06, 2019. https://doi.org/10.31979/etd.h8mt-65ct ; https://scholarworks.sjsu.edu/etd_projects/666.

MLA Handbook (7th Edition):

Deshmukh, Kunal Rajan. “Image Compression Using Neural Networks.” 2019. Web. 06 Dec 2019.

Vancouver:

Deshmukh KR. Image Compression Using Neural Networks. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2019 Dec 06]. Available from: https://doi.org/10.31979/etd.h8mt-65ct ; https://scholarworks.sjsu.edu/etd_projects/666.

Council of Science Editors:

Deshmukh KR. Image Compression Using Neural Networks. [Masters Thesis]. San Jose State University; 2019. Available from: https://doi.org/10.31979/etd.h8mt-65ct ; https://scholarworks.sjsu.edu/etd_projects/666


University of Minnesota

7. Upadhyay, Yash. Equivariance in GAN Critics.

Degree: MS, Computer Science, 2019, University of Minnesota

 Equivariance allows learning a representation that disentangles an entity or a feature from it's meta-properties. Spatially-equivariant representations lead to more detailed representations that can capture… (more)

Subjects/Keywords: Capsule Networks; CNN; Discriminator; Equivariance; Generative Adversarial Networks; invariance

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Upadhyay, Y. (2019). Equivariance in GAN Critics. (Masters Thesis). University of Minnesota. Retrieved from http://hdl.handle.net/11299/206196

Chicago Manual of Style (16th Edition):

Upadhyay, Yash. “Equivariance in GAN Critics.” 2019. Masters Thesis, University of Minnesota. Accessed December 06, 2019. http://hdl.handle.net/11299/206196.

MLA Handbook (7th Edition):

Upadhyay, Yash. “Equivariance in GAN Critics.” 2019. Web. 06 Dec 2019.

Vancouver:

Upadhyay Y. Equivariance in GAN Critics. [Internet] [Masters thesis]. University of Minnesota; 2019. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/11299/206196.

Council of Science Editors:

Upadhyay Y. Equivariance in GAN Critics. [Masters Thesis]. University of Minnesota; 2019. Available from: http://hdl.handle.net/11299/206196


University of Illinois – Urbana-Champaign

8. Subakan, Y. Cem. Generative modeling of sequential data.

Degree: PhD, Computer Science, 2018, University of Illinois – Urbana-Champaign

 In this thesis, we investigate various approaches for generative modeling, with a special emphasis on sequential data. Namely, we develop methodologies to deal with issues… (more)

Subjects/Keywords: Generative Modeling; Sequential Modeling; Generative Adversarial Networks; Probabilistic Modeling; Method of Moments

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Subakan, Y. C. (2018). Generative modeling of sequential data. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/100972

Chicago Manual of Style (16th Edition):

Subakan, Y Cem. “Generative modeling of sequential data.” 2018. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed December 06, 2019. http://hdl.handle.net/2142/100972.

MLA Handbook (7th Edition):

Subakan, Y Cem. “Generative modeling of sequential data.” 2018. Web. 06 Dec 2019.

Vancouver:

Subakan YC. Generative modeling of sequential data. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2018. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/2142/100972.

Council of Science Editors:

Subakan YC. Generative modeling of sequential data. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/100972


University of Georgia

9. Pandhe, Narita Shashikant. Generative spatiotemporal modeling of neutrophil behavior.

Degree: MS, Computer Science, 2017, University of Georgia

 Cell motion and appearance have a strong correlation with cell cycle and disease progression. Many contemporary efforts in machine learning utilize spatiotemporal models to predict… (more)

Subjects/Keywords: Biomedical Imaging; Deep Learning; Computer Vision; Generative Adversarial Networks (GANs); Autoregression

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Pandhe, N. S. (2017). Generative spatiotemporal modeling of neutrophil behavior. (Masters Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/37835

Chicago Manual of Style (16th Edition):

Pandhe, Narita Shashikant. “Generative spatiotemporal modeling of neutrophil behavior.” 2017. Masters Thesis, University of Georgia. Accessed December 06, 2019. http://hdl.handle.net/10724/37835.

MLA Handbook (7th Edition):

Pandhe, Narita Shashikant. “Generative spatiotemporal modeling of neutrophil behavior.” 2017. Web. 06 Dec 2019.

Vancouver:

Pandhe NS. Generative spatiotemporal modeling of neutrophil behavior. [Internet] [Masters thesis]. University of Georgia; 2017. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/10724/37835.

Council of Science Editors:

Pandhe NS. Generative spatiotemporal modeling of neutrophil behavior. [Masters Thesis]. University of Georgia; 2017. Available from: http://hdl.handle.net/10724/37835


Rochester Institute of Technology

10. Longman, Ram. Embedded CycleGAN for Shape-Agnostic Image-to-Image Translation.

Degree: MS, Computer Engineering, 2018, Rochester Institute of Technology

  Image-to-Image translation is the task of translating images between domains while maintaining the identity of the images. The task can be used for entertainment… (more)

Subjects/Keywords: Computer vision; Deep learning; Generative adversarial networks; Machine learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Longman, R. (2018). Embedded CycleGAN for Shape-Agnostic Image-to-Image Translation. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9939

Chicago Manual of Style (16th Edition):

Longman, Ram. “Embedded CycleGAN for Shape-Agnostic Image-to-Image Translation.” 2018. Masters Thesis, Rochester Institute of Technology. Accessed December 06, 2019. https://scholarworks.rit.edu/theses/9939.

MLA Handbook (7th Edition):

Longman, Ram. “Embedded CycleGAN for Shape-Agnostic Image-to-Image Translation.” 2018. Web. 06 Dec 2019.

Vancouver:

Longman R. Embedded CycleGAN for Shape-Agnostic Image-to-Image Translation. [Internet] [Masters thesis]. Rochester Institute of Technology; 2018. [cited 2019 Dec 06]. Available from: https://scholarworks.rit.edu/theses/9939.

Council of Science Editors:

Longman R. Embedded CycleGAN for Shape-Agnostic Image-to-Image Translation. [Masters Thesis]. Rochester Institute of Technology; 2018. Available from: https://scholarworks.rit.edu/theses/9939


University of Bridgeport

11. Alshinina, Remah. A Highly Accurate Deep Learning Based Approach For Developing Wireless Sensor Network Middleware .

Degree: 2018, University of Bridgeport

 Despite the popularity of wireless sensor networks (WSNs) in a wide range of applications, the security problems associated with WSNs have not been completely resolved.… (more)

Subjects/Keywords: Wireless sensor networks (WSN); Machine learning; Security; Generative adversarial network; Discriminator

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Alshinina, R. (2018). A Highly Accurate Deep Learning Based Approach For Developing Wireless Sensor Network Middleware . (Thesis). University of Bridgeport. Retrieved from https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2492

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Alshinina, Remah. “A Highly Accurate Deep Learning Based Approach For Developing Wireless Sensor Network Middleware .” 2018. Thesis, University of Bridgeport. Accessed December 06, 2019. https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2492.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Alshinina, Remah. “A Highly Accurate Deep Learning Based Approach For Developing Wireless Sensor Network Middleware .” 2018. Web. 06 Dec 2019.

Vancouver:

Alshinina R. A Highly Accurate Deep Learning Based Approach For Developing Wireless Sensor Network Middleware . [Internet] [Thesis]. University of Bridgeport; 2018. [cited 2019 Dec 06]. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2492.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Alshinina R. A Highly Accurate Deep Learning Based Approach For Developing Wireless Sensor Network Middleware . [Thesis]. University of Bridgeport; 2018. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/2492

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Rochester Institute of Technology

12. Sweet, Christopher R. Synthesizing Cyber Intrusion Alerts using Generative Adversarial Networks.

Degree: MS, Computer Engineering, 2019, Rochester Institute of Technology

  Cyber attacks infiltrating enterprise computer networks continue to grow in number, severity, and complexity as our reliance on such networks grows. Despite this, proactive… (more)

Subjects/Keywords: Cyber-alerts; Generative adversarial networks; Information theory; Intrusion detection; Machine learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sweet, C. R. (2019). Synthesizing Cyber Intrusion Alerts using Generative Adversarial Networks. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10008

Chicago Manual of Style (16th Edition):

Sweet, Christopher R. “Synthesizing Cyber Intrusion Alerts using Generative Adversarial Networks.” 2019. Masters Thesis, Rochester Institute of Technology. Accessed December 06, 2019. https://scholarworks.rit.edu/theses/10008.

MLA Handbook (7th Edition):

Sweet, Christopher R. “Synthesizing Cyber Intrusion Alerts using Generative Adversarial Networks.” 2019. Web. 06 Dec 2019.

Vancouver:

Sweet CR. Synthesizing Cyber Intrusion Alerts using Generative Adversarial Networks. [Internet] [Masters thesis]. Rochester Institute of Technology; 2019. [cited 2019 Dec 06]. Available from: https://scholarworks.rit.edu/theses/10008.

Council of Science Editors:

Sweet CR. Synthesizing Cyber Intrusion Alerts using Generative Adversarial Networks. [Masters Thesis]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10008


King Abdullah University of Science and Technology

13. Hamdi, Abdullah. Cascading Generative Adversarial Networks for Targeted.

Degree: 2018, King Abdullah University of Science and Technology

 Abundance of labelled data played a crucial role in the recent developments in computer vision, but that faces problems like scalability and transferability to the… (more)

Subjects/Keywords: Artificial Intelligence; computer vision; Generative adversarial Networks; Creative AI; K-NN

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hamdi, A. (2018). Cascading Generative Adversarial Networks for Targeted. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/627557

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Hamdi, Abdullah. “Cascading Generative Adversarial Networks for Targeted.” 2018. Thesis, King Abdullah University of Science and Technology. Accessed December 06, 2019. http://hdl.handle.net/10754/627557.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Hamdi, Abdullah. “Cascading Generative Adversarial Networks for Targeted.” 2018. Web. 06 Dec 2019.

Vancouver:

Hamdi A. Cascading Generative Adversarial Networks for Targeted. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2018. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/10754/627557.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hamdi A. Cascading Generative Adversarial Networks for Targeted. [Thesis]. King Abdullah University of Science and Technology; 2018. Available from: http://hdl.handle.net/10754/627557

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Ottawa

14. Paget, Bryan. An Introduction to Generative Adversarial Networks .

Degree: 2019, University of Ottawa

This thesis is a survey of the mathematical theory of Generative Adversarial Networks (GANs). The relevant theories discussed are game theory, information theory and optimal transport theory.

Subjects/Keywords: game theory; information theory; optimal transport theory; Generative Adversarial Networks

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Paget, B. (2019). An Introduction to Generative Adversarial Networks . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/39603

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Paget, Bryan. “An Introduction to Generative Adversarial Networks .” 2019. Thesis, University of Ottawa. Accessed December 06, 2019. http://hdl.handle.net/10393/39603.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Paget, Bryan. “An Introduction to Generative Adversarial Networks .” 2019. Web. 06 Dec 2019.

Vancouver:

Paget B. An Introduction to Generative Adversarial Networks . [Internet] [Thesis]. University of Ottawa; 2019. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/10393/39603.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Paget B. An Introduction to Generative Adversarial Networks . [Thesis]. University of Ottawa; 2019. Available from: http://hdl.handle.net/10393/39603

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Utah State University

15. Zhang, Kaige. Deep Learning for Crack-Like Object Detection.

Degree: PhD, Computer Science, 2019, Utah State University

  Cracks are common defects on surfaces of man-made structures such as pavements, bridges, walls of nuclear power plants, ceilings of tunnels, etc. Timely discovering… (more)

Subjects/Keywords: deep learning; generative adversarial networks; crack detection; Computer Sciences

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhang, K. (2019). Deep Learning for Crack-Like Object Detection. (Doctoral Dissertation). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/7616

Chicago Manual of Style (16th Edition):

Zhang, Kaige. “Deep Learning for Crack-Like Object Detection.” 2019. Doctoral Dissertation, Utah State University. Accessed December 06, 2019. https://digitalcommons.usu.edu/etd/7616.

MLA Handbook (7th Edition):

Zhang, Kaige. “Deep Learning for Crack-Like Object Detection.” 2019. Web. 06 Dec 2019.

Vancouver:

Zhang K. Deep Learning for Crack-Like Object Detection. [Internet] [Doctoral dissertation]. Utah State University; 2019. [cited 2019 Dec 06]. Available from: https://digitalcommons.usu.edu/etd/7616.

Council of Science Editors:

Zhang K. Deep Learning for Crack-Like Object Detection. [Doctoral Dissertation]. Utah State University; 2019. Available from: https://digitalcommons.usu.edu/etd/7616

16. Bazrafkan, Shabab. Contributions to deep learning methodologies.

Degree: 2018, NUI Galway

 In recent years the Deep Neural Networks (DNN) has been using widely in a big range of machine learning and data-mining purposes. This pattern recognition… (more)

Subjects/Keywords: Deep neural networks; Data augmentation; Generative adversarial networks; Engineering and Informatics; Electrical and Electronic Engineering

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bazrafkan, S. (2018). Contributions to deep learning methodologies. (Thesis). NUI Galway. Retrieved from http://hdl.handle.net/10379/14628

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Bazrafkan, Shabab. “Contributions to deep learning methodologies.” 2018. Thesis, NUI Galway. Accessed December 06, 2019. http://hdl.handle.net/10379/14628.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bazrafkan, Shabab. “Contributions to deep learning methodologies.” 2018. Web. 06 Dec 2019.

Vancouver:

Bazrafkan S. Contributions to deep learning methodologies. [Internet] [Thesis]. NUI Galway; 2018. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/10379/14628.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bazrafkan S. Contributions to deep learning methodologies. [Thesis]. NUI Galway; 2018. Available from: http://hdl.handle.net/10379/14628

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Illinois – Urbana-Champaign

17. Lim, Teck Yian. Audio super-resolution with deep neural networks.

Degree: MS, Electrical & Computer Engr, 2018, University of Illinois – Urbana-Champaign

 This thesis reports various attempts at applying generative deep neural networks to audio for the task of recovering a high quality audio signal when given… (more)

Subjects/Keywords: Deep Neural Networks; Audio; Signal Processing; Generative Adversarial Networks; GAN; DNN; Super resolution

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lim, T. Y. (2018). Audio super-resolution with deep neural networks. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/100932

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lim, Teck Yian. “Audio super-resolution with deep neural networks.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed December 06, 2019. http://hdl.handle.net/2142/100932.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lim, Teck Yian. “Audio super-resolution with deep neural networks.” 2018. Web. 06 Dec 2019.

Vancouver:

Lim TY. Audio super-resolution with deep neural networks. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/2142/100932.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lim TY. Audio super-resolution with deep neural networks. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/100932

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

18. Shmelkov, Konstantin. Approches pour l'apprentissage incrémental et la génération des images : Approaches for incremental learning and image generation.

Degree: Docteur es, Mathématiques et informatique, 2019, Grenoble Alpes

 Cette thèse explore deux sujets liés dans le contexte de l'apprentissage profond : l'apprentissage incrémental et la génération des images. L'apprentissage incrémental étudie l'entrainement des… (more)

Subjects/Keywords: Reseaux adverses generatifs; Réseaux de neurones; Neural networks; Generative adversarial networks; 510

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Shmelkov, K. (2019). Approches pour l'apprentissage incrémental et la génération des images : Approaches for incremental learning and image generation. (Doctoral Dissertation). Grenoble Alpes. Retrieved from http://www.theses.fr/2019GREAM010

Chicago Manual of Style (16th Edition):

Shmelkov, Konstantin. “Approches pour l'apprentissage incrémental et la génération des images : Approaches for incremental learning and image generation.” 2019. Doctoral Dissertation, Grenoble Alpes. Accessed December 06, 2019. http://www.theses.fr/2019GREAM010.

MLA Handbook (7th Edition):

Shmelkov, Konstantin. “Approches pour l'apprentissage incrémental et la génération des images : Approaches for incremental learning and image generation.” 2019. Web. 06 Dec 2019.

Vancouver:

Shmelkov K. Approches pour l'apprentissage incrémental et la génération des images : Approaches for incremental learning and image generation. [Internet] [Doctoral dissertation]. Grenoble Alpes; 2019. [cited 2019 Dec 06]. Available from: http://www.theses.fr/2019GREAM010.

Council of Science Editors:

Shmelkov K. Approches pour l'apprentissage incrémental et la génération des images : Approaches for incremental learning and image generation. [Doctoral Dissertation]. Grenoble Alpes; 2019. Available from: http://www.theses.fr/2019GREAM010


University of Waterloo

19. Zhong, Zilong. Spectral-Spatial Neural Networks and Probabilistic Graph Models for Hyperspectral Image Classification.

Degree: 2019, University of Waterloo

 Pixel-wise hyperspectral image (HSI) classification has been actively studied since it shares similar characteristics with related computer vision tasks, including image classification, object detection, and… (more)

Subjects/Keywords: hyperspectral image classification; spectral-spatial neural networks; generative adversarial networks; probabilistic graphical models

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhong, Z. (2019). Spectral-Spatial Neural Networks and Probabilistic Graph Models for Hyperspectral Image Classification. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14893

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Zhong, Zilong. “Spectral-Spatial Neural Networks and Probabilistic Graph Models for Hyperspectral Image Classification.” 2019. Thesis, University of Waterloo. Accessed December 06, 2019. http://hdl.handle.net/10012/14893.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zhong, Zilong. “Spectral-Spatial Neural Networks and Probabilistic Graph Models for Hyperspectral Image Classification.” 2019. Web. 06 Dec 2019.

Vancouver:

Zhong Z. Spectral-Spatial Neural Networks and Probabilistic Graph Models for Hyperspectral Image Classification. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/10012/14893.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhong Z. Spectral-Spatial Neural Networks and Probabilistic Graph Models for Hyperspectral Image Classification. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/14893

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Florida Atlantic University

20. Shorten, Connor M. An Exploration into Synthetic Data and Generative Aversarial Networks.

Degree: MS, 2019, Florida Atlantic University

This Thesis surveys the landscape of Data Augmentation for image datasets. Completing this survey inspired further study into a method of generative modeling known as… (more)

Subjects/Keywords: Neural networks (Computer science); Computer vision; Images; Generative adversarial networks; Data sets

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Shorten, C. M. (2019). An Exploration into Synthetic Data and Generative Aversarial Networks. (Masters Thesis). Florida Atlantic University. Retrieved from http://fau.digital.flvc.org/islandora/object/fau:41404

Chicago Manual of Style (16th Edition):

Shorten, Connor M. “An Exploration into Synthetic Data and Generative Aversarial Networks.” 2019. Masters Thesis, Florida Atlantic University. Accessed December 06, 2019. http://fau.digital.flvc.org/islandora/object/fau:41404.

MLA Handbook (7th Edition):

Shorten, Connor M. “An Exploration into Synthetic Data and Generative Aversarial Networks.” 2019. Web. 06 Dec 2019.

Vancouver:

Shorten CM. An Exploration into Synthetic Data and Generative Aversarial Networks. [Internet] [Masters thesis]. Florida Atlantic University; 2019. [cited 2019 Dec 06]. Available from: http://fau.digital.flvc.org/islandora/object/fau:41404.

Council of Science Editors:

Shorten CM. An Exploration into Synthetic Data and Generative Aversarial Networks. [Masters Thesis]. Florida Atlantic University; 2019. Available from: http://fau.digital.flvc.org/islandora/object/fau:41404


University of Windsor

21. Mehta, Kaitav Nayankumar. Neuroevolutionary Training of Deep Convolutional Generative Adversarial Networks.

Degree: MS, Computer Science, 2019, University of Windsor

 Recent developments in Deep Learning are noteworthy when it comes to learning the probability distribution of points through neural networks, and one of the crucial… (more)

Subjects/Keywords: evolutionary deep neural networks; Evolutionary GAN; Evolved GANs; Generative Adversarial Networks; Neuroevolution; Neuroevolutionary training

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Mehta, K. N. (2019). Neuroevolutionary Training of Deep Convolutional Generative Adversarial Networks. (Masters Thesis). University of Windsor. Retrieved from https://scholar.uwindsor.ca/etd/7820

Chicago Manual of Style (16th Edition):

Mehta, Kaitav Nayankumar. “Neuroevolutionary Training of Deep Convolutional Generative Adversarial Networks.” 2019. Masters Thesis, University of Windsor. Accessed December 06, 2019. https://scholar.uwindsor.ca/etd/7820.

MLA Handbook (7th Edition):

Mehta, Kaitav Nayankumar. “Neuroevolutionary Training of Deep Convolutional Generative Adversarial Networks.” 2019. Web. 06 Dec 2019.

Vancouver:

Mehta KN. Neuroevolutionary Training of Deep Convolutional Generative Adversarial Networks. [Internet] [Masters thesis]. University of Windsor; 2019. [cited 2019 Dec 06]. Available from: https://scholar.uwindsor.ca/etd/7820.

Council of Science Editors:

Mehta KN. Neuroevolutionary Training of Deep Convolutional Generative Adversarial Networks. [Masters Thesis]. University of Windsor; 2019. Available from: https://scholar.uwindsor.ca/etd/7820


Linköping University

22. Johansson, Philip. Incremental Learning of Deep Convolutional Neural Networks for Tumour Classification in Pathology Images.

Degree: Biomedical Engineering, 2019, Linköping University

  Medical doctors understaffing is becoming a compelling problem in many healthcare systems. This problem can be alleviated by utilising Computer-Aided Diagnosis (CAD) systems to… (more)

Subjects/Keywords: Deep Learning; Convolutional Nerual Networks; Pathology; Incremental Learning; Catastrophic Forgetting; Generative Adversarial Networks; Auxiliary Classification Generative Adversarial Networks; Computer Sciences; Datavetenskap (datalogi); Medical Image Processing; Medicinsk bildbehandling

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Johansson, P. (2019). Incremental Learning of Deep Convolutional Neural Networks for Tumour Classification in Pathology Images. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158225

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Johansson, Philip. “Incremental Learning of Deep Convolutional Neural Networks for Tumour Classification in Pathology Images.” 2019. Thesis, Linköping University. Accessed December 06, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158225.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Johansson, Philip. “Incremental Learning of Deep Convolutional Neural Networks for Tumour Classification in Pathology Images.” 2019. Web. 06 Dec 2019.

Vancouver:

Johansson P. Incremental Learning of Deep Convolutional Neural Networks for Tumour Classification in Pathology Images. [Internet] [Thesis]. Linköping University; 2019. [cited 2019 Dec 06]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158225.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Johansson P. Incremental Learning of Deep Convolutional Neural Networks for Tumour Classification in Pathology Images. [Thesis]. Linköping University; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158225

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Duke University

23. Gan, Zhe. Deep Generative Models for Vision and Language Intelligence .

Degree: 2018, Duke University

  Deep generative models have achieved tremendous success in recent years, with applications in various tasks involving vision and language intelligence. In this dissertation, I… (more)

Subjects/Keywords: Artificial intelligence; Electrical engineering; Computer science; deep generative models; deep learning; generative adversarial networks; machine learning; sigmoid belief networks; visual captioning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Gan, Z. (2018). Deep Generative Models for Vision and Language Intelligence . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/16810

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Gan, Zhe. “Deep Generative Models for Vision and Language Intelligence .” 2018. Thesis, Duke University. Accessed December 06, 2019. http://hdl.handle.net/10161/16810.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Gan, Zhe. “Deep Generative Models for Vision and Language Intelligence .” 2018. Web. 06 Dec 2019.

Vancouver:

Gan Z. Deep Generative Models for Vision and Language Intelligence . [Internet] [Thesis]. Duke University; 2018. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/10161/16810.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gan Z. Deep Generative Models for Vision and Language Intelligence . [Thesis]. Duke University; 2018. Available from: http://hdl.handle.net/10161/16810

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


KTH

24. Pakdaman, Hesam. Updating the generator in PPGN-h with gradients flowing through the encoder.

Degree: Electrical Engineering and Computer Science (EECS), 2018, KTH

<em>The Generative Adversarial Network framework has shown success in implicitly modeling data distributions and is able to generate realistic samples. Its architecture is comprised… (more)

Subjects/Keywords: Computer Science; Computer Vision; Deep Learning; Machine Learning; Generative Adversarial Networks; GAN; Neural Networks; Generative models; Computer Sciences; Datavetenskap (datalogi)

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Pakdaman, H. (2018). Updating the generator in PPGN-h with gradients flowing through the encoder. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-224867

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Pakdaman, Hesam. “Updating the generator in PPGN-h with gradients flowing through the encoder.” 2018. Thesis, KTH. Accessed December 06, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-224867.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Pakdaman, Hesam. “Updating the generator in PPGN-h with gradients flowing through the encoder.” 2018. Web. 06 Dec 2019.

Vancouver:

Pakdaman H. Updating the generator in PPGN-h with gradients flowing through the encoder. [Internet] [Thesis]. KTH; 2018. [cited 2019 Dec 06]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-224867.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pakdaman H. Updating the generator in PPGN-h with gradients flowing through the encoder. [Thesis]. KTH; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-224867

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Arizona State University

25. YEH, HUAI-MING. Image-based Process Monitoring via Generative Adversarial Autoencoder with Applications to Rolling Defect Detection.

Degree: Industrial Engineering, 2019, Arizona State University

 Image-based process monitoring has recently attracted increasing attention due to the advancement of the sensing technologies. However, existing process monitoring methods fail to fully utilize… (more)

Subjects/Keywords: Industrial engineering; Information technology; Computer science; adversarial autoencoder; anomaly detection; generative adversarial networks; machine learning; statistic; unsupervised learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

YEH, H. (2019). Image-based Process Monitoring via Generative Adversarial Autoencoder with Applications to Rolling Defect Detection. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/53733

Chicago Manual of Style (16th Edition):

YEH, HUAI-MING. “Image-based Process Monitoring via Generative Adversarial Autoencoder with Applications to Rolling Defect Detection.” 2019. Masters Thesis, Arizona State University. Accessed December 06, 2019. http://repository.asu.edu/items/53733.

MLA Handbook (7th Edition):

YEH, HUAI-MING. “Image-based Process Monitoring via Generative Adversarial Autoencoder with Applications to Rolling Defect Detection.” 2019. Web. 06 Dec 2019.

Vancouver:

YEH H. Image-based Process Monitoring via Generative Adversarial Autoencoder with Applications to Rolling Defect Detection. [Internet] [Masters thesis]. Arizona State University; 2019. [cited 2019 Dec 06]. Available from: http://repository.asu.edu/items/53733.

Council of Science Editors:

YEH H. Image-based Process Monitoring via Generative Adversarial Autoencoder with Applications to Rolling Defect Detection. [Masters Thesis]. Arizona State University; 2019. Available from: http://repository.asu.edu/items/53733


KTH

26. Gawande, Saurabh. Generative adversarial networks for single image super resolution in microscopy images.

Degree: Electrical Engineering and Computer Science (EECS), 2018, KTH

Image Super resolution is a widely-studied problem in computer vision, where the objective is to convert a lowresolution image to a high resolution image.… (more)

Subjects/Keywords: Deep Learning; Generative adversarial networks; Super resolution; High content screening microscopy; Deep Learning; Generative adversarial networks; Super resolution; High content screening microscopy; Computer and Information Sciences; Data- och informationsvetenskap

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Gawande, S. (2018). Generative adversarial networks for single image super resolution in microscopy images. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230188

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Gawande, Saurabh. “Generative adversarial networks for single image super resolution in microscopy images.” 2018. Thesis, KTH. Accessed December 06, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230188.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Gawande, Saurabh. “Generative adversarial networks for single image super resolution in microscopy images.” 2018. Web. 06 Dec 2019.

Vancouver:

Gawande S. Generative adversarial networks for single image super resolution in microscopy images. [Internet] [Thesis]. KTH; 2018. [cited 2019 Dec 06]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230188.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gawande S. Generative adversarial networks for single image super resolution in microscopy images. [Thesis]. KTH; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230188

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Duke University

27. Li, Chunyuan. Towards Better Representations with Deep/Bayesian Learning .

Degree: 2018, Duke University

  Deep learning and Bayesian Learning are two popular research topics in machine learning. They provide the flexible representations in the complementary manner. Therefore, it… (more)

Subjects/Keywords: Artificial intelligence; Statistics; Computer science; adversarial learning; Bayesian learning; deep learning; generative models; neural networks

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Li, C. (2018). Towards Better Representations with Deep/Bayesian Learning . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/18207

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Li, Chunyuan. “Towards Better Representations with Deep/Bayesian Learning .” 2018. Thesis, Duke University. Accessed December 06, 2019. http://hdl.handle.net/10161/18207.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Li, Chunyuan. “Towards Better Representations with Deep/Bayesian Learning .” 2018. Web. 06 Dec 2019.

Vancouver:

Li C. Towards Better Representations with Deep/Bayesian Learning . [Internet] [Thesis]. Duke University; 2018. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/10161/18207.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li C. Towards Better Representations with Deep/Bayesian Learning . [Thesis]. Duke University; 2018. Available from: http://hdl.handle.net/10161/18207

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Minnesota

28. Fabbri, Cameron. Enhancing Visual Perception in Noisy Environments using Generative Adversarial Networks.

Degree: MS, Computer Science, 2018, University of Minnesota

 Autonomous robots rely on a variety of sensors – acoustic, inertial, and visual – for intelligent decision making. Due to its non-intrusive, passive nature, and… (more)

Subjects/Keywords: Color Correction; Deep Learning; Generative Adversarial Networks; Machine Learning; Robotics; Underwater Vision

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Fabbri, C. (2018). Enhancing Visual Perception in Noisy Environments using Generative Adversarial Networks. (Masters Thesis). University of Minnesota. Retrieved from http://hdl.handle.net/11299/201008

Chicago Manual of Style (16th Edition):

Fabbri, Cameron. “Enhancing Visual Perception in Noisy Environments using Generative Adversarial Networks.” 2018. Masters Thesis, University of Minnesota. Accessed December 06, 2019. http://hdl.handle.net/11299/201008.

MLA Handbook (7th Edition):

Fabbri, Cameron. “Enhancing Visual Perception in Noisy Environments using Generative Adversarial Networks.” 2018. Web. 06 Dec 2019.

Vancouver:

Fabbri C. Enhancing Visual Perception in Noisy Environments using Generative Adversarial Networks. [Internet] [Masters thesis]. University of Minnesota; 2018. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/11299/201008.

Council of Science Editors:

Fabbri C. Enhancing Visual Perception in Noisy Environments using Generative Adversarial Networks. [Masters Thesis]. University of Minnesota; 2018. Available from: http://hdl.handle.net/11299/201008


King Abdullah University of Science and Technology

29. Aljaafari, Nura. Ichthyoplankton Classification Tool using Generative Adversarial Networks and Transfer Learning.

Degree: 2018, King Abdullah University of Science and Technology

 The study and the analysis of marine ecosystems is a significant part of the marine science research. These systems are valuable resources for fisheries, improving… (more)

Subjects/Keywords: Deep learning; transfer learning; ichthyoplankton; semi-supervised learning; marine; Generative adversarial Networks

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Aljaafari, N. (2018). Ichthyoplankton Classification Tool using Generative Adversarial Networks and Transfer Learning. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/627578

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Aljaafari, Nura. “Ichthyoplankton Classification Tool using Generative Adversarial Networks and Transfer Learning.” 2018. Thesis, King Abdullah University of Science and Technology. Accessed December 06, 2019. http://hdl.handle.net/10754/627578.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Aljaafari, Nura. “Ichthyoplankton Classification Tool using Generative Adversarial Networks and Transfer Learning.” 2018. Web. 06 Dec 2019.

Vancouver:

Aljaafari N. Ichthyoplankton Classification Tool using Generative Adversarial Networks and Transfer Learning. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2018. [cited 2019 Dec 06]. Available from: http://hdl.handle.net/10754/627578.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Aljaafari N. Ichthyoplankton Classification Tool using Generative Adversarial Networks and Transfer Learning. [Thesis]. King Abdullah University of Science and Technology; 2018. Available from: http://hdl.handle.net/10754/627578

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Linköping University

30. Hörnstedt, Julia Hagvall. Synthesis of Thoracic Computer Tomography Images using Generative Adversarial Networks.

Degree: Division of Biomedical Engineering, 2019, Linköping University

  The use of machine learning algorithms to enhance and facilitate medical diagnosis and analysis is a promising and an important area, which could improve… (more)

Subjects/Keywords: Generative Adversarial Networks; deep learning; image synthesis; synthetic images; image segmentation; Medical Engineering; Medicinteknik

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hörnstedt, J. H. (2019). Synthesis of Thoracic Computer Tomography Images using Generative Adversarial Networks. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158280

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Hörnstedt, Julia Hagvall. “Synthesis of Thoracic Computer Tomography Images using Generative Adversarial Networks.” 2019. Thesis, Linköping University. Accessed December 06, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158280.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Hörnstedt, Julia Hagvall. “Synthesis of Thoracic Computer Tomography Images using Generative Adversarial Networks.” 2019. Web. 06 Dec 2019.

Vancouver:

Hörnstedt JH. Synthesis of Thoracic Computer Tomography Images using Generative Adversarial Networks. [Internet] [Thesis]. Linköping University; 2019. [cited 2019 Dec 06]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158280.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hörnstedt JH. Synthesis of Thoracic Computer Tomography Images using Generative Adversarial Networks. [Thesis]. Linköping University; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158280

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

[1] [2]

.