CNN-heatmap

1. Visualizing and Understanding Convolutional Networks
https://arxiv.org/pdf/1311.2901v3.pdf
https://neukom.dartmouth.edu/docs/bbat-wacv2016.pdf
http://cs231n.github.io/understanding-cnn/


Net surgery trick
http://cs231n.github.io/convolutional-networks/#convert
https://github.com/BVLC/caffe/blob/master/examples/net_surgery.ipynb

https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/image_segmentation.html
https://arxiv.org/pdf/1502.02766v3.pdf
https://arxiv.org/pdf/1411.4038v2.pdf
http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf
https://arxiv.org/pdf/1605.06211v1.pdf
convert fully connected layers to their     equivalent convolutional layers, since the weights are the same and only     the shapes are different.

Global average pooling layer
Network In Network
https://arxiv.org/pdf/1312.4400.pdf

Learning Deep Features for Discriminative Localization
http://cnnlocalization.csail.mit.edu/
https://github.com/jacobgil/keras-cam

Grad-CAM: Gradient-weighted Class Activation Mapping
https://github.com/ramprs/grad-cam
http://gradcam.cloudcv.org/
https://arxiv.org/pdf/1610.02391v2.pdf

Is object localization for free? – Weakly Supervised Object Recognition with Convolutional Neural Networks
http://www.di.ens.fr/willow/research/weakcnn/
http://www.di.ens.fr/willow/research/cnn/

Additional: ~~~

1. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps https://arxiv.org/pdf/1312.6034v2.pdf

2. Top-down NeuralAttention by Excitation Backprop (c-MWP) https://arxiv.org/pdf/1608.00507v1.pdf http://cs-people.bu.edu/jmzhang/excitationbp.html

3. Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer https://arxiv.org/pdf/1612.03928v1.pdf https://github.com/szagoruyko/attention-transfer

4. Visualizing Deep Neural Network Decisions: Prediction Difference Analysis https://openreview.net/pdf?id=BJ5UeU9xx https://github.com/lmzintgraf/DeepVis-PredDiff A New Method to Visualize Deep Neural Networks https://icmlviz.github.io/assets/papers/23.pdf

5. Self-Taught Object Localization with Deep Network https://arxiv.org/pdf/1409.3964.pdf

6. Shallow and Deep Convolutional Networks for Saliency Prediction https://github.com/imatge-upc/saliency-2016-cvpr ~~~

Keras codebase: ~~~ https://github.com/raghakot/keras-vis

Oclusion based technique: https://github.com/waleedka/cnn-visualization/blob/master/cnn_visualization.ipynb

Net surgery trick: https://github.com/heuritech/convnets-keras

GAP-CAM https://github.com/alexisbcook/ResNetCAM-keras/ https://github.com/jacobgil/keras-cam https://github.com/tdeboissiere/VGG16CAM-keras https://github.com/keras-team/keras/blob/0cfa5c2709906a7a76f552f71a562f899e408695/examples/classactivationmaps.py

Grad-CAM https://github.com/jacobgil/keras-grad-cam https://github.com/hiveml/tensorflow-grad-cam

https://raghakot.github.io/keras-vis/visualizations/attention/

https://github.com/mlhy/ResNet-50-for-Cats.Vs.Dogs ~~~

Tensorflow codebase: ~~~ GAP https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/gap_mnist.ipynb

GAP-CAM https://github.com/jazzsaxmafia/Weakly_detector

Grad-CAM https://github.com/Ankush96/grad-cam.tensorflow

PyTorch codebase:

Grad-CAM https://github.com/kazuto1011/grad-cam-pytorch https://github.com/jacobgil/pytorch-grad-cam

GAP-CAM https://github.com/metalbubble/CAM/blob/master/pytorch_CAM.py

SPN https://github.com/yeezhu/SPN.pytorch

https://github.com/utkuozbulak/pytorch-cnn-visualizations

https://github.com/jacobgil/pytorch-explain-black-box ~~~

Regression Activation Map https://github.com/cauchyturing/kagglediabeticRAM

Other ~~~ https://github.com/metalbubble/cnnvisualizer

https://github.com/InFoCusp/tf_cnnvis

https://github.com/ppwwyyxx/tensorpack/tree/master/examples/Saliency

https://github.com/InFoCusp/tf_cnnvis

https://github.com/keplr-io/quiver

https://jacobgil.github.io/deeplearning/vehicle-steering-angle-visualizations https://jacobgil.github.io/deeplearning/class-activation-maps

https://jacobgil.github.io/deeplearning/filter-visualizations https://jacobgil.github.io/computervision/saliency-from-backproj

https://github.com/CSAILVision/NetDissect

https://medium.com/merantix/picasso-a-free-open-source-visualizer-for-cnns-d8ed3a35cfc5

https://github.com/CSAILVision/NetDissect http://netdissect.csail.mit.edu/

https://github.com/imatge-upc/saliency-salgan-2017

http://imatge-upc.github.io/saliency-2016-cvpr/ https://github.com/imatge-upc/saliency-2016-cvpr

https://openreview.net/pdf?id=SkfMWhAqYQ

https://thegradient.pub/a-visual-history-of-interpretation-for-image-recognition/ https://thegradient.pub/interpretability-in-ml-a-broad-overview/

Visualization:

https://github.com/shaohua0116/Activation-Visualization-Histogram ~~~

To look at: ~~~ http://blog.qure.ai/notes/visualizingdeeplearning https://github.com/utkuozbulak/pytorch-cnn-visualizations https://github.com/fornaxai/receptivefield ~~~