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About Deep Residual Learning For Image Recognition (Summary)
1) Deep residual neural networks allow training of much deeper models than plain networks without degradation in performance. This is achieved by learning residual functions with "shortcut connections" that add the input to the output. 2) Residual networks were able to reduce both training and validation errors with increasing depth on ImageNet classification, with a 110-layer ResNet achieving better performance than plain networks. 3) Residual connections help training by making the learning of residual functions easier than learning unreferenced functions, and produce smaller responses in layers compared to plain networks.
Detailed Information
| Author: | ['Tomoki Tsuchida'] |
|---|---|
| Publication Year: | 2016 |
| Pages: | 11 |
| Language: | English |
| Format: | |
| Price: | FREE |
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