Principal Feature Visualization (PFV)

Principal feature visualization is a visualization technique for convolutional neural networks that highlights the contrasting features in a batch of images. It produces one RGB heatmap per input image.

Dependencies

• pytorch
• numpy

Additional dependencies for the demo:

• torchvision
• matplotlib
• pillow

Getting started

Install the dependencies listed above, and run the example in demo.py: python demo.py

Example

A trained network shows good localization:

But an untrained (re-initialized) network shows scrambled output, as expected:

The paper

This method was presented at ECCV 2020. Please see the full paper and supplementary material for more information about our method.

If you find this useful, please cite:

@inproceedings{bakken2020principal,
  title={Principal Feature Visualisation in Convolutional Neural Networks},
  author={Bakken, Marianne and Kvam, Johannes and Stepanov, Alexey A and Berge, Asbj{\o}rn},
  booktitle={European Conference on Computer Vision},
  pages={18--31},
  year={2020},
  organization={Springer}
}