CNN及其可解释性

标签：amp   gen   sim   vol   learning   www   des   cnn   represent   

https://stats385.github.io/readings

https://www.quora.com/What-is-a-convolutional-neural-network

https://distill.pub/2017/feature-visualization/

https://colah.github.io/posts/2014-07-Conv-Nets-Modular/

http://neuralnetworksanddeeplearning.com/chap6.html

https://distill.pub/2018/building-blocks/

https://cs231n.github.io/neural-networks-1/

https://distill.pub/2016/deconv-checkerboard/

https://www.tensorflow.org/tutorials/images/deep_cnn

Unsupervised representation learning with deep convolutional generative adversarial networks  [PDF]025

1. Inceptionism: Going deeper into neural networks  [HTML]
   Mordvintsev, A., Olah, C. and Tyka, M., 2015. Google Research Blog. Retrieved June, Vol 20.
2. Geodesics of learned representations  [PDF]
   Henaff, O.J. and Simoncelli, E.P., 2015. arXiv preprint arXiv:1511.06394.
3. DeepDreaming with TensorFlow  [link]

1. A guide to convolution arithmetic for deep learning  [PDF]

1. Dumoulin, V. and Visin, F., 2016. arXiv preprint arXiv:1603.07285.
2. Is the deconvolution layer the same as a convolutional layer?  [PDF]
   Shi, W., Caballero, J., Theis, L., Huszar, F., Aitken, A., Ledig, C. and Wang, Z., 2016. arXiv preprint arXiv:1609.07009.
3. Conditional generative adversarial nets for convolutional face generation  [PDF]
   Gauthier, J., 2014. Class Project for Stanford CS231N: Convolutional Neural Networks for Visual Recognition, Winter semester, Vol 2014.

CNN及其可解释性

标签：amp   gen   sim   vol   learning   www   des   cnn   represent   

原文地址：https://www.cnblogs.com/WCFGROUP/p/9656748.html