Interpretability of Machine Learning-Visualizations

Gradient-weighted Class Activation Mapping (Grad-CAM)

Pycaffe implementation of the paper Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

For this implementation I'm using a pretrained image classification model downloaded from the community in Caffe Model Zoo.

For this example, I will use BVLC reference caffenet model which is trained to classify images into 1000 classes. To download the model, go to the folder where you installed Caffe, e.g. C:\Caffe and run

 ./scripts/download_model_binary.py models/bvlc_reference_caffenet
 
./data/ilsvrc12/get_ilsvrc_aux.sh

Original Image	GradCAM	Guided GradCAM

Guided Backpropagation

Pycaffe implementation of Guided Back Propagation introduced in Striving for Simplicity : The All Convolutional Net.

Original Image	Guided Backpropagation