20160302.md

• AlexNet

  • http://arxiv.org/abs/1409.0575
  • http://vision.stanford.edu/teaching/cs231b_spring1415/slides/alexnet_tugce_kyunghee.pdf
  • http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

• ResNet

  • http://arxiv.org/abs/1512.03385

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

  • https://github.com/shiyemin/ResNet-Generator-for-caffe

  • http://cs231n.stanford.edu/reports2016/264_Report.pdf

  • https://www.reddit.com/r/MachineLearning/comments/441125/highway_networks_are_to_deep_residual_networks/

  • http://arxiv.org/abs/1505.00387

  • http://arxiv.org/abs/1507.06228

  • https://arxiv.org/abs/1604.03640

  • https://arxiv.org/abs/1603.05027

  • http://arxiv.org/abs/1603.09382

  • http://icml.cc/2016/tutorials/icml2016_tutorial_deep_residual_networks_kaiminghe.pdf

  • https://blog.init.ai/residual-neural-networks-are-an-exciting-area-of-deep-learning-research-acf14f4912e9

  • http://arxiv.org/abs/1602.04485

  • http://arxiv.org/abs/1603.08029

  • http://arxiv.org/abs/1604.04112

  • https://github.com/facebook/fb.resnet.torch

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

  • https://github.com/XiaozhiChen/resnet-generator

  • rbgirshick/py-faster-rcnn#62

  • https://github.com/jay-mahadeokar/pynetbuilder

  • http://www.jianshu.com/p/b724411571ab

  • https://arxiv.org/abs/1605.06431

  • http://chuansong.me/n/362841051449

  • https://arxiv.org/abs/1605.07716

  • https://arxiv.org/abs/1605.07648

  • http://chuansong.me/n/353691251997

  • https://www.reddit.com/r/MachineLearning/comments/4klqq9/160506431_residual_networks_are_exponential/

  • http://arxiv.org/abs/1507.01526

  • https://www.gitbook.com/book/yingchenlu/famous-convolutional-neural-networks

  • http://yanran.li/peppypapers/2015/11/28/improving-information-flow-in-recurrent-networks.html

  • http://yanran.li/peppypapers/2016/01/10/highway-networks-and-deep-residual-networks.html

  • http://arxiv.org/abs/1608.06993

  • http://arxiv.org/abs/1409.5185

  • https://github.com/s9xie/DSN

  • https://arxiv.org/abs/1402.1869

  • http://arxiv.org/abs/1511.08228

  • https://arxiv.org/abs/1603.08983

  • http://arxiv.org/abs/1606.05262

  • https://arxiv.org/abs/1605.07146

  • https://github.com/szagoruyko/wide-residual-networks

  • https://arxiv.org/abs/1611.10080

  • https://github.com/itijyou/ademxapp

  • https://arxiv.org/abs/1701.02362

• Vgg

  • http://www.robots.ox.ac.uk/~vgg/research/very_deep/
  • http://arxiv.org/abs/1409.1556
  • http://arxiv.org/abs/1405.3531
  • http://www.robots.ox.ac.uk/~vgg/publications/2014/Chatfield14/presentation.pdf
  • http://stackoverflow.com/questions/28232235/how-to-calculate-the-number-of-parameters-of-convolutional-neural-networkscnns

• LeNet

  • http://www.cnblogs.com/yymn/articles/4553666.html
  • http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf
  • http://systemdesign.altera.com/can-you-see-using-convolutional-neural-networks/

• Inception

  • https://arxiv.org/abs/1409.4842
  • http://arxiv.org/abs/1512.00567
  • https://arxiv.org/pdf/1602.07261.pdf
  • http://www.jianshu.com/p/329d2c0cfca9
  • http://blog.csdn.net/u010025211/article/details/51206237
  • http://blog.csdn.net/shuzfan/article/details/50738394
  • https://huixucs.wordpress.com/2016/03/08/笔记inception-v4-inception-resnet-and-the-impact-of-residual-connections-on-learning/
  • https://www.reddit.com/r/MachineLearning/comments/2gp385/googlenet_slides_from_eccv_2014_workshop/
  • http://image-net.org/challenges/LSVRC/2014/slides/GoogLeNet.pptx
  • BVLC/caffe#3067
  • http://stackoverflow.com/questions/30585122/how-to-calculate-the-number-of-parameters-for-google-net
  • https://github.com/lim0606/caffe-googlenet-bn
  • https://arxiv.org/abs/1610.02357