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.}
Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research fkahe, v-xiangz, v-shren, jiansung@microsoft.com Abstract 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 ...
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