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. This result won the 1st place on theILSVRC 2015 classification task. We also present analysison CIFAR-10 with 100 and 1000 depth of representations is of central importancefor many visual Recognition tasks.}
tremely deep representations, we obtain a 28% relative im-provement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet local-ization, COCO detection, and COCO segmentation. 1. Introduction
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