This is a Python3 / Tensorflow implementation of the methods proposed in the paper:
Diverse feature visualizations reveal invariances in early layers of deep neural networks, by Santiago Cadena, Marissa Weis, Leon Gatys, Matthias Bethge, and Alexander Ecker.
Take a look at the two sample notebooks for the Diverse Visualizations of a paricular feature map of VGG19, and the shift-invariance test propsed in the paper.
To run this code you need the following:
- Python3
- Matplotlib
- Tensorflow
- Download the checkpoint weights of the normalized VGG network here (80MB), as well as the pixelcnn++ here or here if the later is broken (656MB), and store them in the networks/ folder
Our code uses the open-AI implementation of PixelCNN++ that can be found here.
If you find our code useful please cite us in your work:
@article{cadena2018diverse,
title={Diverse feature visualizations reveal invariances in early layers of deep neural networks},
author={Cadena, Santiago A and Weis, Marissa A and Gatys, Leon A and Bethge, Matthias and Ecker, Alexander S},
journal={arXiv preprint arXiv:1807.10589},
year={2018}
}