Resources on ResNet

github: https://github.com/KaimingHe/deep-residual-networks

youtube video: https://www.youtube.com/watch?v=C6tLw-rPQ2o

slide: https://pdfs.semanticscholar.org/presentation/276e/8e23f8232b55193b4c1917150e77549a4675.pdf

Quite related:

  •  Convolutional Neural Networks at Constrained Time Cost (https://arxiv.org/pdf/1412.1710.pdf) Interesting predecessor of the paper.
  • Highway networks: (https://arxiv.org/pdf/1505.00387.pdf)

Unprocessed but Good:

  • multigrid tutorial (https://www.math.ust.hk/~mawang/teaching/math532/mgtut.pdf)
  • https://blog.waya.ai/deep-residual-learning-9610bb62c355 (Talk about Resnet, Wide Resnet and ResXnet)
  • Wide Residual Networks (https://arxiv.org/pdf/1605.07146.pdf)
  • Aggregated Residual Transformations for Deep Neural Networks (https://arxiv.org/pdf/1611.05431.pdf)
  • https://www.kdnuggets.com/2016/09/deep-learning-reading-group-deep-residual-learning-image-recognition.html
  • Deep Networks with Stochastic Depth https://arxiv.org/abs/1603.09382
  • Highway network: https://arxiv.org/pdf/1505.00387.pdf
  • http://www.deeplearningpatterns.com/doku.php?id=residual
  • Ablation study: http://torch.ch/blog/2016/02/04/resnets.html
  • It's implemented in TF: https://www.quora.com/Have-the-ideas-of-Deep-Residual-Learning-for-Image-Recognition-be-implemented-in-TensorFlow
  • Wider or Deeper: Revisiting the ResNet Model for Visual Recognition: https://arxiv.org/abs/1611.10080
  • Deep Residual Learning and PDEs on Manifold: http://ymsc.tsinghua.edu.cn/~shizqi/papers/ResNet_PDE.pdf
  • Is it really because of ensemble? https://ai.stackexchange.com/questions/1997/resnets-ensemble-or-depth-makes-residual-networks-strong
  • https://vision.cornell.edu/se3/wp-content/uploads/2017/04/ResNet_Ensemble_NIPS.pdf
  • Multi-level Residual Networks from Dynamical Systems View (https://openreview.net/pdf?id=SyJS-OgR-)
  • Exploring Normalization in Deep Residual Networks with Concatenated Rectified Linear Units (https://research.fb.com/wp-content/uploads/2017/01/paper_expl_norm_on_deep_res_networks.pdf?)
  • TinyImageNet (http://cs231n.stanford.edu/reports/2016/pdfs/411_Report.pdf)
  • Predict Cortical Representation (https://www.nature.com/articles/s41598-018-22160-9)

Another summary:

https://www.commonlounge.com/discussion/839d11b9a67d464796e5ba0309611e9b

Leave a Reply

Your email address will not be published. Required fields are marked *