Connexion saute-couche

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ResNet and its constituent residual blocks draw their names from the ‘residual’—the difference between the predicted and target values. The authors of ResNet used residual learning of the form H(x) = F(x) + x. Simply, this means that even in the case of no residual, F(x)=0, we would still preserve an identity mapping of the input, x. The resulting learned residual allows our network to theoretically do no worse (than without it).