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- Residual networks
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- 152 layers
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- Skips every two layers
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- Residual block
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- Later layers learning the identity function
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- Skips help
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- Deep network should be at least as good as shallower one by allowing some layers to do very little
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- Vanishing gradient
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- Allows shortcut paths for gradients
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- Accuracy saturation
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- Adding more layers to suitably deep network increases training error
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# Design
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- Skips across pairs of [conv layers](../Convolutional%20Layer.md)
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- Elementwise addition
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- All layer 3x3 kernel
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- Spatial size halves each layer
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- Filters doubles each layer
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- [Fully convolutional](FCN.md)
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- No fc layer
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- No [pooling](../Max%20Pooling.md)
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- Except at end
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- No dropout
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[ImageNet](../../CV/Datasets.md#ImageNet) Error:
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![imagenet-error](../../../../img/imagenet-error.png)
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2023-05-27 23:02:51 +01:00
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![resnet-arch](../../../../img/resnet-arch.png)
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![resnet-arch2](../../../../img/resnet-arch2.png)
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