avik-pal / CNNVisualize.jl

CNN Visualizations in Flux

CNNVisualize.jl

This Package implements popular CNN Visualization techniques and is built on top of Flux.jl. Most of the models from Metalhead.jl will work out of the box. To visualize custom models look at the documentation.

Implemented Algorithms

1. Gradient Visualizations using Vanilla BackPropagation
2. Gradient Visualizations using DeconvNet
3. Gradient Visualizations using Guided BackPropagation
4. Gradient Weight Class Activation Maps
5. Guided Gradient Weight Class Activation Maps
6. DeepDream
7. Class Specific Image Generation

TODO

1. CNN Filter Visualization
2. Inverted Image Representations
3. Smooth Grad

Some Notes

1. DeepDream Code in this repo is a direct copy of the code in my other repository DeepDream.jl. The code here demonstrates only a small fraction of what is actually implemented in the original repository. For more advanced operations like guided dreams and video generation have a look at the other repository

References

[1] J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller. Striving for Simplicity: The All Convolutional Net, https://arxiv.org/abs/1412.6806

[2] B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, A. Torralba. Learning Deep Features for Discriminative Localization, https://arxiv.org/abs/1512.04150

[3] R. R. Selvaraju, A. Das, R. Vedantam, M. Cogswell, D. Parikh, and D. Batra. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization, https://arxiv.org/abs/1610.02391

[4] K. Simonyan, A. Vedaldi, A. Zisserman. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, https://arxiv.org/abs/1312.6034

[5] A. Mahendran, A. Vedaldi. Understanding Deep Image Representations by Inverting Them, https://arxiv.org/abs/1412.0035

[6] H. Noh, S. Hong, B. Han, Learning Deconvolution Network for Semantic Segmentation https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Noh_Learning_Deconvolution_Network_ICCV_2015_paper.pdf

[7] A. Nguyen, J. Yosinski, J. Clune. Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images https://arxiv.org/abs/1412.1897

[8] D. Smilkov, N. Thorat, N. Kim, F. Viégas, M. Wattenberg. SmoothGrad: removing noise by adding noise https://arxiv.org/abs/1706.03825

[9] D. Erhan, Y. Bengio, A. Courville, P. Vincent. Visualizing Higher-Layer Features of a Deep Network https://www.researchgate.net/publication/265022827_Visualizing_Higher-Layer_Features_of_a_Deep_Network

[10] A. Mordvintsev, C. Olah, M. Tyka. Inceptionism: Going Deeper into Neural Networks https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html

This repo draws deep inspiration from https://github.com/utkuozbulak/pytorch-cnn-visualizations which implements similar algorithms in Pytorch