sar-gupta / Convisualize_nb
Licence: mit
Visualisations for Convolutional Neural Networks in Pytorch
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convisualize
Visualizations for Convolutional Neural Networks (CNNs) in Pytorch
The corresponding article can be found here!
Requirements:
- Pytorch
- Torchvision
- Numpy
- Matplotlib
- Pillow
Note: In case you don't have a GPU, remove all instances of "cuda" and "cpu" from the notebook before running.
TODO
- [X] Layer Outputs at all layers
- [X] Filter outputs at a given layer
- [X] Filter visualization at a given layer
- [X] Image heatmap using Occlusion
- [ ] Image heatmap using Grad Cam
- [X] Class specific saliency maps
- [X] SmoothGrad
- [ ] Semantic segmentation using GrabCut
- [X] Visualization of class models (Gradient Ascent)
- [ ] Regularization techniques for class models (L2, Clip, Blur, etc.)
- [ ] Guided Backprop
- [ ] Filter visualization (Gradient Ascent)
- [ ] Neural Texture Synthesis
- [ ] Deep Dream
- [ ] Neural Style Transfer
References
- cs231n
- cs231n Lecture 12
- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
- Visualizing and Understanding Convolutional Networks Matthew D Zeiler, Rob Fergus
- SmoothGrad: removing noise by adding noise Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, Martin Wattenberg
- Texture Synthesis Using Convolutional Neural Networks Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
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