Deep Residual Learning for Image Recognition

Posted 2008nmj

tags:

篇首语:本文由小常识网(cha138.com)小编为大家整理,主要介绍了Deep Residual Learning for Image Recognition相关的知识,希望对你有一定的参考价值。

Kaiming He
Xiangyu Zhang
Shaoqing Ren
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 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 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 complex-
ity. 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 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 ex-
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 competitions 1 , where we also won the 1st
places on the tasks of ImageNet detection, ImageNet local-
ization, COCO detection, and COCO segmentation.

以上是关于Deep Residual Learning for Image Recognition的主要内容,如果未能解决你的问题,请参考以下文章

论文笔记:Deep Residual Learning

Deep Residual Learning for Image Recognition(MSRA-深度残差学习)

PyTorch笔记 - ResNet: Deep Residual Learning for Image Recognition

PyTorch笔记 - ResNet: Deep Residual Learning for Image Recognition

PyTorch笔记 - ResNet: Deep Residual Learning for Image Recognition

Deep Residual Learning for Image Recognition(残差网络)