This repository is an implementation of the paper Discovering and Explaining the Representation Bottleneck of DNNs (arxiv), which was accepted as an Oral presentation at ICLR 2022.
- Python 3.9
- pytorch 1.7.1
- CUDA 11.0
- numpy 1.19.5
- torchvision 0.8.2
All models were trained on a single NVIDIA GeForce RTX 3090 GPU.
Run the following shell script to train the models:
./train.shYou can change the gpu by changing the --gpu_id argument in the script.
The models are saved in the checkpoints directory by default.
Run the following shell script to compute interaction for the models:
./interaction.sh
You can uncomment the setting you want to run on top of the script.
The results are saved in the results directory by default.
If you use this project in your research, please cite it.
@inproceedings{
deng2022discovering,
title={{Discovering} and {Explaining} the {Representation} {Bottleneck} of {DNNs}},
author={Huiqi Deng and Qihan Ren and Hao Zhang and Quanshi Zhang},
booktitle={International Conference on Learning Representations},
year={2022}
}