Файл:Deep residual learning for image recognition 1512.03385v1.pdf

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Deep_residual_learning_for_image_recognition_1512.03385v1.pdf(0 × 0 пикселей, размер файла: 800 КБ, MIME-тип: application/pdf)

Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research {kahe, v-xiangz, v-shren, jiansun}@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 as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networksareeasiertooptimize,andcangainaccuracyfrom considerably increased depth. On the ImageNet dataset we evaluate residualnets with adepth of up to152 layers—8× deeper than VGG nets [41] but still having lower complexity. Anensembleoftheseresidualnetsachieves3.57%error ontheImageNettestset. Thisresultwonthe1stplaceonthe ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residualnetsarefoundationsofoursubmissionstoILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

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