Proceedings Article10.1109/SIBGRAPI-T.2019.00007
Perfect Storm: DSAs Embrace Deep Learning for GPU-Based Computer Vision
Marcelo Pias,Silvia Silva da Costa Botelho,Paulo Drews +2 more
- 01 Oct 2019
- pp 8-21
2
TL;DR: This paper explores Domain-Specific Deep Learning Architectures for GPU Computer Vision through a "brainstorming" approach on selected hands-on topics in the area through tools, frameworks and data pipelines commonly used to train and deploy DNNs in GPUs and Domain- Specific Architectures (DSAs).
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Abstract: This paper explores Domain-Specific Deep Learning Architectures for GPU Computer Vision through a "brainstorming" approach on selected hands-on topics in the area. We intend to discuss Deep Neural Networks (DNNs) to image classification problems through tools, frameworks and data pipelines commonly used to train and deploy DNNs in GPUs and Domain-Specific Architectures (DSAs).
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Citations
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References
Deep Residual Learning for Image Recognition
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- 27 Jun 2016
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
•Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton +2 more
- 03 Dec 2012
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
67K
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
- 07 Jun 2015
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).