Network Dissection Lite in PyTorch

Introduction

This repository is fork of NetDissect, which contains the demo code for the work Network Dissection: Quantifying Interpretability of Deep Visual Representations. We also test the Network Dissection results on ResNet18 and ResNet18+CBAM. This code is written in pytorch and python3.6, tested on Ubuntu 20.04(google colab).

Originally written by: David Bau∗ , Bolei Zhou∗ , Aditya Khosla, Aude Oliva, and Antonio Torralba

Reimplemented and extended by: Ruifeng Xu, Yuhang Mei

Classification for ResNet18 and ResNet18+CBAM on CIFAR and ImageNet

• Go to the 'Classification' folder
• Run in google colab:
  • TrainingOnCifar10Dataset - CIFAR10
  • TrainingOnCifar100Dataset - CIFAR100
  • TrainingOnImageNet - ImageNet

Run NetDissect for ResNet18 and ResNet18+CBAM

• Go to the 'ND' folder

• Run in google colab

Reference

@inproceedings{netdissect2017,
  title={Network Dissection: Quantifying Interpretability of Deep Visual Representations},
  author={Bau, David and Zhou, Bolei and Khosla, Aditya and Oliva, Aude and Torralba, Antonio},
  booktitle={Computer Vision and Pattern Recognition},
  year={2017}
}