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/
2. 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.`
3. Global average pooling layer
Network In Network
https://arxiv.org/pdf/1312.4400.pdf
4. Learning Deep Features for Discriminative Localization
http://cnnlocalization.csail.mit.edu/
https://github.com/jacobgil/keras-cam
5. Grad-CAM: Gradient-weighted Class Activation Mapping
https://github.com/ramprs/grad-cam
http://gradcam.cloudcv.org/
https://arxiv.org/pdf/1610.02391v2.pdf
6. 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/class_activation_maps.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/kaggle_diabetic_RAM
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/visualizing_deep_learning
https://github.com/utkuozbulak/pytorch-cnn-visualizations
https://github.com/fornaxai/receptivefield