Resnet BN

【深度学习】深入理解Batch Normalization批标准化

https://www.zhihu.com/topic/20084849/hot

resnet（残差网络）的F（x）究竟长什么样子？

https://www.zhihu.com/question/53224378

如何理解微软的深度残差学习？

https://www.zhihu.com/question/38499534?sort=created

 

SKIP CONNECTIONS ELIMINATE SINGULARITIES

https://arxiv.org/pdf/1701.09175.pdf

 详解残差网络

https://zhuanlan.zhihu.com/p/42706477

 

残差网络原理

https://blog.csdn.net/qq_30478885/article/details/78828734

https://www.coursera.org/lecture/convolutional-neural-networks/why-resnets-work-XAKNO

https://arxiv.org/pdf/1512.03385.pdf

https://www.quora.com/How-does-deep-residual-learning-work

https://arxiv.org/pdf/1603.05027.pdf

Resnet中残差块的作用是完成恒等变换，那这样的恒等变换的意义是什么，在网络中能起到怎样的作用呢？

https://www.zhihu.com/question/293243905

https://zhuanlan.zhihu.com/p/28124810 

https://arxiv.org/pdf/1502.03167v3.pdf

https://zhuanlan.zhihu.com/p/31645196

https://www.coursera.org/lecture/convolutional-neural-networks/why-resnets-work-XAKNO

https://arxiv.org/pdf/1506.01497v3.pdf

https://arxiv.org/pdf/1504.08083.pdf

https://arxiv.org/pdf/1311.2524v5.pdf

https://arxiv.org/pdf/1702.08591.pdf

https://arxiv.org/pdf/1611.05431.pdf

https://arxiv.org/pdf/1607.07032.pdf

 

https://arxiv.org/abs/1605.06431

Residual Networks 理解

协方差

https://www.zhihu.com/question/20852004

ResNet架构可逆！多大等提出性能优越的可逆残差网络

一文简述ResNet及其多种变体

 

TensorFlow 实现 Resnet V2 代码解读

Identity Mapping in ResNet

1. 学习吴恩达在coursera的“深度学习课程”中关于残差网络的内容
2. 读该模型的原版论文：Deep Residual Learning for Image Recognition，如果阅读有难度，可以参考网络上的翻译稿，这里有一篇笔者的翻译稿供参考。
3. 注册github，用于查看和下载残差网络的开源源码。注册地址。
4. 复制源代码到本地。源码地址在此。

 

【1】He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.

【2】Srivastava R K, Greff K, Schmidhuber J. Highway networks[J]. arXiv preprint arXiv:1505.00387, 2015.

【3】Orhan A E, Pitkow X. Skip connections eliminate singularities[J]. arXiv preprint arXiv:1701.09175, 2017.

【4】Shang W, Sohn K, Almeida D, et al. Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units[J]. 2016:2217-2225.

【5】Greff K, Srivastava R K, Schmidhuber J. Highway and Residual Networks learn Unrolled Iterative Estimation[J]. 2017.

【6】Jastrzebski S, Arpit D, Ballas N, et al. Residual connections encourage iterative inference[J]. arXiv preprint arXiv:1710.04773, 2017.