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

Dates: Last 2 Years

You searched for subject:(Fine to Coarse). Showing records 1 – 2 of 2 total matches.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


Delft University of Technology

1. Lelekas, Ioannis (author). Top-Down Networks: A coarse-to-fine reimagination of CNNs.

Degree: 2020, Delft University of Technology

Biological vision adopts a coarse-to-fine information processing pathway, from initial visual detection and binding of salient features of a visual scene, to the enhanced and preferential processing given relevant stimuli. On the contrary, CNNs employ a fine-to-coarse processing, moving from local, edge-detecting filters to more global ones extracting abstract representations of the input. In the current paper we propose the extraction of top-down networks, by reversing the feature extraction part of the baseline, bottom-up architecture. This coarse-to-fine pathway, by blurring out higher frequency information and restoring it only at later stages, offers a line of defence against attacks introducing high frequency noise. High resolution of the final convolutional layer's feature map can contribute to the transparency of the network's decision making process, as well as favor more object-driven decisions over context driven ones and thus provide better localized class activation maps. The paper offers empirical evidence for the applicability of the method to various existing architectures, but also on multiple visual recognition tasks. Advisors/Committee Members: van Gemert, Jan (mentor), Reinders, Marcel (graduation committee), Vos, Frans (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Computer Vision; Deep Learning; Convolutional Neural Networks; Top-Down; Fine-to-Coarse; Coarse-to-Fine; Adversarial attacks; Adversarial robustness; Gradcam; Object localization

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Lelekas, I. (. (2020). Top-Down Networks: A coarse-to-fine reimagination of CNNs. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345

Chicago Manual of Style (16th Edition):

Lelekas, Ioannis (author). “Top-Down Networks: A coarse-to-fine reimagination of CNNs.” 2020. Masters Thesis, Delft University of Technology. Accessed April 16, 2021. http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345.

MLA Handbook (7th Edition):

Lelekas, Ioannis (author). “Top-Down Networks: A coarse-to-fine reimagination of CNNs.” 2020. Web. 16 Apr 2021.

Vancouver:

Lelekas I(. Top-Down Networks: A coarse-to-fine reimagination of CNNs. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Apr 16]. Available from: http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345.

Council of Science Editors:

Lelekas I(. Top-Down Networks: A coarse-to-fine reimagination of CNNs. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:11888a7b-1e54-424d-9daa-8ff48de58345

2. Honari, Sina. Feature extraction on faces : from landmark localization to depth estimation.

Degree: 2019, Université de Montréal

Subjects/Keywords: Neural networks; Deep learning; Convolutional networks; Supervised learning; Unsupervised learning; Semi-supervised learning; Coarse-to-fine architectures; Landmark localization; Depth estimation; Face rotation; Face replacement; Réseaux neuronaux; Apprentissage profond; Réseaux neuronaux de convolution; Apprentissage supervisé; Apprentissage non-supervisé; Apprentissage semi-supervisé; Architectures grossières à fines; Localisation de points clés; Estimation de la profondeur; Rotation de visage; Échange de visage; Applied Sciences - Artificial Intelligence / Sciences appliqués et technologie - Intelligence artificielle (UMI : 0800)

…83 vii Chapter 6. Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation… …52 Chapter 3. Prologue to First Article… …79 Chapter 5. Prologue to Second Article… …101 Chapter 7. Prologue to Third Article… …135 Chapter 9. Prologue to Fourth Article… 

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Honari, S. (2019). Feature extraction on faces : from landmark localization to depth estimation. (Thesis). Université de Montréal. Retrieved from http://hdl.handle.net/1866/22658

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):

Honari, Sina. “Feature extraction on faces : from landmark localization to depth estimation.” 2019. Thesis, Université de Montréal. Accessed April 16, 2021. http://hdl.handle.net/1866/22658.

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

MLA Handbook (7th Edition):

Honari, Sina. “Feature extraction on faces : from landmark localization to depth estimation.” 2019. Web. 16 Apr 2021.

Vancouver:

Honari S. Feature extraction on faces : from landmark localization to depth estimation. [Internet] [Thesis]. Université de Montréal; 2019. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/1866/22658.

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

Council of Science Editors:

Honari S. Feature extraction on faces : from landmark localization to depth estimation. [Thesis]. Université de Montréal; 2019. Available from: http://hdl.handle.net/1866/22658

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

.