ResNet basics (Deep Residual Network for Image Recognition)
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The document presents the deep residual learning framework proposed in the paper "Deep Residual Learning for Image Recognition". The framework aims to make it easier to optimize extremely deep convolutional neural networks. It does this by introducing "skip connections" that allow layers to learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. This addresses issues like vanishing gradients in very deep networks. The authors demonstrate that residual networks are easier to optimize and can gain accuracy from increased depth, outperforming standard networks.
![Plain Block
𝑎[𝑙] 𝑎[𝑙+2]
𝑎[𝑙+1]
𝑧[𝑙+1]
= 𝑊[𝑙+1]
𝑎[𝑙]
+ 𝑏[𝑙+1]
“linear”
𝑎[𝑙+1] = 𝑔(𝑧[𝑙+1])
“relu”
𝑧[𝑙+2] = 𝑊[𝑙+2] 𝑎[𝑙+1] + 𝑏[𝑙+2]
“output”
𝑎[𝑙+2]
= 𝑔 𝑧 𝑙+2
“relu on output”
Image source: deeplearning.ai
Presented by – Sanjay Saha (sanjaysaha@u.nus.edu) School of Computing](https://image.slidesharecdn.com/resnetpresentationsanjay-200506032258/85/ResNet-basics-Deep-Residual-Network-for-Image-Recognition-9-320.jpg)
![Residual Block
𝑎[𝑙] 𝑎[𝑙+2]
𝑎[𝑙+1]
𝑧[𝑙+1]
= 𝑊[𝑙+1]
𝑎[𝑙]
+ 𝑏[𝑙+1]
“linear”
𝑎[𝑙+1] = 𝑔(𝑧[𝑙+1])
“relu”
𝑧[𝑙+2] = 𝑊[𝑙+2] 𝑎[𝑙+1] + 𝑏[𝑙+2]
“output”
𝑎[𝑙+2]
= 𝑔 𝑧 𝑙+2
+ 𝑎 𝑙
“relu on output plus input”
Presented by – Sanjay Saha (sanjaysaha@u.nus.edu)
Image source: deeplearning.ai
School of Computing](https://image.slidesharecdn.com/resnetpresentationsanjay-200506032258/85/ResNet-basics-Deep-Residual-Network-for-Image-Recognition-10-320.jpg)
![Summary – Advantages of ResNet over Plain
Networks
• A deeper plain network tends to perform bad because of the
vanishing and exploding gradients
• In such cases, ResNets will stop improving rather than decrease in
performance: 𝑎[𝑙+2] = 𝑔 𝑧 𝑙+2 + 𝑎 𝑙 = 𝑔(𝑤 𝑙+1 𝑎 𝑙+1 + 𝑏 𝑙 + 𝑎[𝑙])
• If a layer is not ‘useful’, L2 regularization will bring its parameters very
close to zero, resulting in 𝑎[𝑙+2]
= 𝑔 𝑎[𝑙]
= 𝑎[𝑙]
(when using ReLU)
• In theory, ResNet is still identical to plain networks, but in practice
due to the above the convergence is much faster.
• No additional training parameters and complexity introduced.
Presented by – Sanjay Saha (sanjaysaha@u.nus.edu) School of Computing](https://image.slidesharecdn.com/resnetpresentationsanjay-200506032258/85/ResNet-basics-Deep-Residual-Network-for-Image-Recognition-22-320.jpg)
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ResNet basics (Deep Residual Network for Image Recognition)
- 1. Deep Residual Learning for Image Recognition Authors: Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun Presented by – Sanjay Saha, School of Computing, NUS CS6240 – Multimedia Analysis – Sem 2 AY2019/20
- 2. Objective | Problem Statement Presented by – Sanjay Saha ([email protected]) School of Computing
- 3. Motivation Performance of plain networks in a deeper architecture Image source: paper Presented by – Sanjay Saha ([email protected]) School of Computing
- 4. Main Idea • Skip Connections/ Shortcuts • Trying to avoid: ‘Vanishing Gradients’ ‘Long training times’ Image source: Wikipedia Presented by – Sanjay Saha ([email protected]) School of Computing
- 5. Contributions | Problem Statement • These extremely deep residual nets are easy to optimize, but the counterpart “plain” nets (that simply stack layers) exhibit higher training error when the depth increases. • These deep residual nets can easily enjoy accuracy gains from greatly increased depth, producing results substantially better than previous networks. Presented by – Sanjay Saha ([email protected]) A residual learning framework to ease the training of networks that are substantially deeper than those used previously. Perfor mance Depth School of Computing
- 6. Literature Presented by – Sanjay Saha ([email protected]) School of Computing
- 7. Literature Review • Partial solutions for vanishing • Batch Normalization – To rescale the weights over some batch. • Smart Initialization of weights – Like for example Xavier initialization. • Train portions of the network individually. • Highway Networks • Feature residual connections of the form 𝑌 = 𝑓 𝑥 × 𝑠𝑖𝑔𝑚𝑜𝑖𝑑(𝑊𝑥 + 𝑏) + 𝑥 × (1 − 𝑠𝑖𝑔𝑚𝑜𝑖𝑑 𝑊𝑥 + 𝑏 ) • Data-dependent gated shortcuts with parameters • When gates are ‘closed’, the layers become ‘non-residual’. Presented by – Sanjay Saha ([email protected]) School of Computing
- 8. ResNet | Design | Architecture Presented by – Sanjay Saha ([email protected]) School of Computing
- 9. Plain Block 𝑎[𝑙] 𝑎[𝑙+2] 𝑎[𝑙+1] 𝑧[𝑙+1] = 𝑊[𝑙+1] 𝑎[𝑙] + 𝑏[𝑙+1] “linear” 𝑎[𝑙+1] = 𝑔(𝑧[𝑙+1]) “relu” 𝑧[𝑙+2] = 𝑊[𝑙+2] 𝑎[𝑙+1] + 𝑏[𝑙+2] “output” 𝑎[𝑙+2] = 𝑔 𝑧 𝑙+2 “relu on output” Image source: deeplearning.ai Presented by – Sanjay Saha ([email protected]) School of Computing
- 10. Residual Block 𝑎[𝑙] 𝑎[𝑙+2] 𝑎[𝑙+1] 𝑧[𝑙+1] = 𝑊[𝑙+1] 𝑎[𝑙] + 𝑏[𝑙+1] “linear” 𝑎[𝑙+1] = 𝑔(𝑧[𝑙+1]) “relu” 𝑧[𝑙+2] = 𝑊[𝑙+2] 𝑎[𝑙+1] + 𝑏[𝑙+2] “output” 𝑎[𝑙+2] = 𝑔 𝑧 𝑙+2 + 𝑎 𝑙 “relu on output plus input” Presented by – Sanjay Saha ([email protected]) Image source: deeplearning.ai School of Computing
- 11. Skip Connections • Skipping immediate connections! • Referred to as residual part of the network. • Such residual part receives the input as an amplifier to its output – The dimensions usually are the same. • Another option is to use a projection to the output space. • Either way – no additional training parameters are used. Image source: towardsdatascience.com Presented by – Sanjay Saha ([email protected]) School of Computing
- 12. ResNet Architecture Image source: paper Presented by – Sanjay Saha ([email protected]) School of Computing
- 13. ResNet Architecture Presented by – Sanjay Saha ([email protected]) Image source: paper Stacked Residual Blocks School of Computing
- 14. ResNet Architecture Presented by – Sanjay Saha ([email protected]) Image source: paper 3x3 conv layers 2x # of filters 2 strides to down-sample Avg. pool after the last conv layer FC layer to output classes School of Computing
- 15. ResNet Architecture Presented by – Sanjay Saha ([email protected]) Image source: paper 3x3 conv layers 2x # of filters 2 strides to down-sample Avg. pool after the last conv layer FC layer to output classes School of Computing
- 16. ResNet Architecture Presented by – Sanjay Saha ([email protected]) Image source: paper 3x3 conv layers 2x # of filters 2 strides to down-sample Avg. pool after the last conv layer FC layer to output classes School of Computing
- 17. ResNet Architecture Presented by – Sanjay Saha ([email protected]) 1x1 conv with 64 filters 28x28x64 Input: 28x28x256 Image source: paper School of Computing
- 18. ResNet Architecture Presented by – Sanjay Saha ([email protected]) 1x1 conv with 64 filters 28x28x64 Input: 28x28x256 3x3 conv on 64 feature maps only Image source: paper School of Computing
- 19. ResNet Architecture Presented by – Sanjay Saha ([email protected]) 1x1 conv with 64 filters 28x28x64 Input: 28x28x256 3x3 conv on 64 feature maps only 1x1 conv with 256 filters 28x28x256 BOTTLENECK Image source: paper School of Computing
- 20. Summary | Advantages Presented by – Sanjay Saha ([email protected]) School of Computing
- 21. Benefits of Bottleneck • Less training time for deeper networks • By keeping time complexity same as two-layer conv. • Hence, allows to increase # of layers. • And, model converges faster: 152- layer ResNet has 11.3 billion FLOPS while VGG-16/19 nets has 15.3/19.6 billion FLOPS. Presented by – Sanjay Saha ([email protected]) Input: 28x28x256 Image source: paper School of Computing
- 22. Summary – Advantages of ResNet over Plain Networks • A deeper plain network tends to perform bad because of the vanishing and exploding gradients • In such cases, ResNets will stop improving rather than decrease in performance: 𝑎[𝑙+2] = 𝑔 𝑧 𝑙+2 + 𝑎 𝑙 = 𝑔(𝑤 𝑙+1 𝑎 𝑙+1 + 𝑏 𝑙 + 𝑎[𝑙]) • If a layer is not ‘useful’, L2 regularization will bring its parameters very close to zero, resulting in 𝑎[𝑙+2] = 𝑔 𝑎[𝑙] = 𝑎[𝑙] (when using ReLU) • In theory, ResNet is still identical to plain networks, but in practice due to the above the convergence is much faster. • No additional training parameters and complexity introduced. Presented by – Sanjay Saha ([email protected]) School of Computing
- 23. Results Presented by – Sanjay Saha ([email protected]) School of Computing
- 24. Results • ILSVRC 2015 classification winner (3.6% top 5 error) -- better than “human performance”! Presented by – Sanjay Saha ([email protected]) Error rates (%) of ensembles. The top-5 error is on the test set of ImageNet and reported by the test server School of Computing
- 25. Results Presented by – Sanjay Saha ([email protected]) Error rates (%, 10-crop testing) on ImageNet validation set Error rates (%) of single-model results on the ImageNet validation set School of Computing
- 26. Plain vs. ResNet Presented by – Sanjay Saha ([email protected]) Image source: paper School of Computing
- 27. Plain vs. Deeper ResNet Presented by – Sanjay Saha ([email protected]) Image source: paper School of Computing
- 28. Conclusion | Future Trends Presented by – Sanjay Saha ([email protected]) School of Computing
- 29. Conclusion •Easy to optimize deep neural networks. •Guaranteed Accuracy gain with deeper layers. •Addressed: Vanishing Gradient and Longer Training duration. Presented by – Sanjay Saha ([email protected]) School of Computing
- 30. Conclusion •Easy to optimize deep neural networks. •Guaranteed Accuracy gain with deeper layers. •Addressed: Vanishing Gradient and Longer Training duration. Presented by – Sanjay Saha ([email protected]) School of Computing
- 31. Conclusion •Easy to optimize deep neural networks. •Guaranteed Accuracy gain with deeper layers. •Addressed: Vanishing Gradient and Longer Training duration. Presented by – Sanjay Saha ([email protected]) School of Computing
- 32. Conclusion •Easy to optimize deep neural networks. •Guaranteed Accuracy gain with deeper layers. •Addressed: Vanishing Gradient and Longer Training duration. Presented by – Sanjay Saha ([email protected]) School of Computing
- 33. Future Trends • Identity Mappings in Deep Residual Networks suggests to pass the input directly to the final residual layer, hence allowing the network to easily learn to pass the input as identity mapping both in forward and backward passes. (He et. al. 2016) • Using the Batch Normalization as pre-activation improves the regularization • Reduce Learning Time with Random Layer Drops • ResNeXt: Aggregated Residual Transformations for Deep Neural Networks. (Xie et. al. 2016) Presented by – Sanjay Saha ([email protected]) School of Computing
- 34. Presented by – Sanjay Saha ([email protected]) School of Computing Questions?