Implement the Guided-ReLU visualization used in the paper:
saliency-maps.py takes an image, and produce its saliency map by running a ResNet-50 and backprop its maximum
activations back to the input image space.
Similar techinques can be used to visualize the concept learned by each filter in the network.
Usage:
wget http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz
tar -xzvf resnet_v1_50_2016_08_28.tar.gz
./saliency-maps.py cat.jpg 
Left to right:
- the original cat image
- the magnitude in the saliency map
- the magnitude blended with the original image
- positive correlated pixels (keep original color)
- negative correlated pixels (keep original color)