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 [41] but still having lower complex-ity. An ensemble of these Residual nets achieves erroron the ImageNettestset.}
learning better networks as easy as stacking more layers? An obstacle to answering this question was the notorious problem of vanishing/exploding gradients [1,9], which however, has been largely addressed by normalized initial-ization [23,9,37,13] and intermediate normalization layers [16], which enable networks with tens of layers to start con-
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