Deep Residual Learning for Image Recognition
underlying mapping as H(x), we let the stacked nonlinear layers fit another mapping of F(x) := H(x) x. The orig-inal mapping is recast into F(x)+x. We hypothesize that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push
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