论文名称：Deep Residual Learning for Image Recognition
论文下载：https://arxiv.org/pdf/1512.03385.pdf
代码下载：
作者：Kaiming He，Xiangyu Zhang，Shaoqing Ren，Jian Sun（AI天团）
发表年份：2016
发表机构：CVPR
地位：解决网络退化的问题，2016年CVPR最佳论文
建议配合阅读：Identity Mapping in Deep Residual Networks
*Deeper neural networks are more diffificult 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 networks are easier to optimize, and can gain accuracy from considerably increased depth**. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8 deeper than VGG nets [41] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet* test *set. This result won the 1st place on the ILSVRC 2015 classifification 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 residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions*1*, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.*