IBD: Interpretable Basis Decomposition for Visual Explanation

Introduction

This repository contains the demo code for the ECCV'18 paper "Interpretable Basis Decomposition for Visual Explanation".

Download

Clone the code of Network Dissection Lite from github

    git clone https://github.com/CSAILVision/IBD
    cd IBD

Download the Broden dataset (~1GB space) and the example pretrained model. If you already download this, you can create a symbolic link to your original dataset.

    ./script/dlbroden.sh
    ./script/dlzoo.sh

Note that AlexNet models work with 227x227 image input, while VGG, ResNet, GoogLeNet works with 224x224 image input.

Requirements

Python Environments

    pip3 install numpy sklearn scipy scikit-image matplotlib easydict torch torchvision

Note: The repo was written by pytorch-0.3.1. (PyTorch, Torchvision)

Run IBD in PyTorch

You can configure settings.py to load your own model, or change the default parameters.
Run IBD

    python3 test.py

IBD Result

At the end of the dissection script, a HTML-formatted report will be generated inside result folder that summarizes the interpretable units of the tested network.

Train Concept Basis

If you want to train the concept basis, delete the pretrained files first.

    rm result/pytorch_resnet18_places365/snapshot/14.pth 
    rm result/pytorch_resnet18_places365/decompose.npy

Run the train script.

    python3 train.py

Then run IBD.

    python3 test.py

Reference

If you find the codes useful, please cite this paper

@inproceedings{IBD2018,
  title={Interpretable Basis Decomposition for Visual Explanation},
  author={Zhou, Bolei* and Sun, Yiyou* and Bau, David* and Torralba, Antonio},
  booktitle={European Conference on Computer Vision},
  year={2018}
}

CSAILVision / IBD