Residual Networks, or ResNets, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack residual blocks ontop of each other to form network: e.g. a ResNet-50 has fifty layers using these blocks.
Formally, denoting the desired underlying mapping as $\mathcal{H}(x)$, we let the stacked nonlinear layers fit another mapping of $\mathcal{F}(x):=\mathcal{H}(x)-x$. The original mapping is recast into $\mathcal{F}(x)+x$.
There is empirical evidence that these types of network are easier to optimize, and can gain accuracy from considerably increased depth.
Source: Deep Residual Learning for Image RecognitionPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Image Classification | 66 | 9.91% |
Self-Supervised Learning | 56 | 8.41% |
Classification | 27 | 4.05% |
Semantic Segmentation | 25 | 3.75% |
Object Detection | 16 | 2.40% |
Quantization | 13 | 1.95% |
Image Segmentation | 9 | 1.35% |
Denoising | 8 | 1.20% |
Benchmarking | 7 | 1.05% |
Component | Type |
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Batch Normalization
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Normalization | (optional) |
Convolution
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Convolutions | |
Global Average Pooling
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Pooling Operations | |
Kaiming Initialization
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Initialization | |
Max Pooling
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Pooling Operations | |
ReLU
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Activation Functions | (optional) |