Transcription of Deep Residual Learning for Image Recognition
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Deep Residual Learning for Image Recognition
Deep Residual Learning for Image RecognitionKaiming HeXiangyu ZhangShaoqing RenJian SunMicrosoft Research{kahe, v-xiangz, v-shren, neural networks are more difficult to train. Wepresent a Residual Learning framework to ease the trainingof networks that are substantially deeper than those usedpreviously. We explicitly reformulate the layers as learn-ing Residual functions with reference to the layer inputs, in-stead of Learning unreferenced functions. We provide com-prehensive empirical evidence showing that these residualnetworks are easier to optimize, and can gain accuracy fromconsiderably increased depth. On the ImageNet dataset weevaluate Residual nets with a depth of up to 152 layers 8 deeper than VGG nets [40] but still having lower complex-ity. An ensemble of these Residual nets achieves erroron the ImageNettestset. This result won the 1st place on theILSVRC 2015 classification task.}
A building block is shown in Fig. 2. Formally, in this paper we consider a building block defined as: y=F(x,{Wi})+x. (1) Here xand yare the input and output vectors of the lay-ers considered. The function F(x,{Wi}) represents the residual mapping to be learned. For the example in Fig. 2 that has two layers, F = W2σ(W1x)in which σ denotes
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