MatConvNet Saliency Visualization

This is a MatConvNet demo of several saliency visualization methods of ConvNet models.

References

1. Error backpropagation: Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: Visualising image classification models and saliency maps.
2. Class Activation Map: Zhou, B., Khosla, A., Lapedriza, A., Oliva, A [[GitHub](https://github.com/jimmie33/Caffe-ExcitationBP]., Torralba, A.: Learning deep features for discriminative localization. [GitHub]
3. Excitation backpropagation: Jianming Zhang, Zhe Lin, Jonathan Brandt, Xiaohui Shen, Stan Sclaroff. Top-down Neural Attention by Excitation Backprop.) [GitHub]

Prerequisites

1. MatConvNet.
2. A trained ConvNet model e.g. ResNet-152 or others here.

Installation

1. Compile MatConvNet.
2. Download the model from the links above, the default model of the demo is the ResNet-152 and place in /Models.
3. Replace the default Conv.m and Pooling.m files in the default MatConvNet folder /MatConvNet/matlab/+dagnn with the ones included.