andy
25f73797e3
Affected files: .obsidian/graph.json .obsidian/workspace.json STEM/AI/Neural Networks/Activation Functions.md STEM/AI/Neural Networks/CNN/CNN.md STEM/AI/Neural Networks/CNN/FCN/FCN.md STEM/AI/Neural Networks/CNN/FCN/FlowNet.md STEM/AI/Neural Networks/CNN/FCN/Highway Networks.md STEM/AI/Neural Networks/CNN/FCN/ResNet.md STEM/AI/Neural Networks/CNN/FCN/Skip Connections.md STEM/AI/Neural Networks/CNN/GAN/DC-GAN.md STEM/AI/Neural Networks/CNN/GAN/GAN.md STEM/AI/Neural Networks/CNN/UpConv.md STEM/img/highway-vs-residual.png STEM/img/imagenet-error.png STEM/img/resnet-arch.png STEM/img/resnet-arch2.png STEM/img/skip-connections 1.png STEM/img/upconv-matrix-result.png STEM/img/upconv-matrix-transposed-result.png STEM/img/upconv-matrix.png STEM/img/upconv-transposed-matrix.png STEM/img/upconv.png
747 B
747 B
- Residual networks
- 152 layers
- Skips every two layers
- Residual block
- Later layers learning the identity function
- Skips help
- Deep network should be at least as good as shallower one by allowing some layers to do very little
- Vanishing gradient
- Allows shortcut paths for gradients
- Accuracy saturation
- Adding more layers to suitably deep network increases training error
Design
- Skips across pairs of Convolutional Layer
- Elementwise addition
- All layer 3x3 kernel
- Spatial size halves each layer
- Filters doubles each layer
- FCN
- No fc layer
- No Max Pooling
- Except at end
- No dropout