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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}
}