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:

You searched for id:"handle:10012/16539". One record found.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


University of Waterloo

1. Real, Shahriar. Triple Pool Net: A novel robust Convolution neural network for image/content classification.

Degree: 2020, University of Waterloo

With the rise of artificial intelligence and machine learning, it is highly desired to find a more efficient neural network architecture for real-life applications. In this paper we propose a novel convolution neural network (CNN) architecture known as triple-pool network (TP-Net), to achieve light-weight training and classification processes. We will firstly provide a comprehensive review on the state-of-the-art, and give a detailed description on the proposed TP-Net. To verify its efficiency, extensive experiments are conducted to compare its performance in terms of training time, error rate (or accuracy), and CPU load in flops, to a number of recently reported CNN architectures, where a well-known publicly available datesets, including CIFAR 10/100, German traffic signs, and SVHN. The network is designed for a convolution neural network(CNN) and can be readily used for image classification or even light weight authentication. We have carefully designed the network to address the gradient vanishing problem that persists in several larger neural network architectures and also addressed the problem of feature loss while reducing dimensions in the pooling layer.

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Real, S. (2020). Triple Pool Net: A novel robust Convolution neural network for image/content classification. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/16539

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):

Real, Shahriar. “Triple Pool Net: A novel robust Convolution neural network for image/content classification.” 2020. Thesis, University of Waterloo. Accessed January 16, 2021. http://hdl.handle.net/10012/16539.

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

MLA Handbook (7th Edition):

Real, Shahriar. “Triple Pool Net: A novel robust Convolution neural network for image/content classification.” 2020. Web. 16 Jan 2021.

Vancouver:

Real S. Triple Pool Net: A novel robust Convolution neural network for image/content classification. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2021 Jan 16]. Available from: http://hdl.handle.net/10012/16539.

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

Council of Science Editors:

Real S. Triple Pool Net: A novel robust Convolution neural network for image/content classification. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/16539

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

.