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Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functio
Deep residual networks were shown to be able to scale up to thousands of layers
and still have improving performance. However, each fraction of a percent of improved
accuracy costs nearly doubling the number of layers, and so training very deep res
