A Machine Teaching Framework for Scalable Recognition

Last update: Dec 8, 2021

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Deep Learning MEMORABLE

Overview

MEMORABLE

This repository contains the source code accompanying our ICCV 2021 paper.

A Machine Teaching Framework for Scalable Recognition
Pei Wang, Nuno Vasconcelos.
In ICCV, 2021.

@InProceedings{wang2021gradient,
author = {Wang, Pei and Vasconcelos, Nuno},
title = {A Machine Teaching Framework for Scalable Recognition},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021}
}

Requirements

The project was implemented and tested in Python 3.5 and Pytorch 1.0. Other versions should work after minor modification.
NVIDIA GPU and cuDNN are required to have fast speeds. For now, CUDA 8.0 with cuDNN 6.0.20 has been tested. The other versions should be working.

Datasets

Butterflies and Chinese Characters, Gull are used. Please organize them as below after download,

datasets
|_ butterflies_crop
  |_ images
    |_ Viceroy
    |_ ...
|_ chinese_chars
  |_ images
    |_ grass
    |_ ...
|_ CUBgull
  |_ images
    |_ CaliforniaGull
    |_ ...

Implementation details

To generate counterfactual explanations

get_all_CE_butterflies.py
get_all_CE_gull.py

by SimCLR model

get_all_CE_butterflies_simclr.py
get_all_CE_gull_simclr.py

To generate teaching images and explanations

train_butterflies_CMaxGrad.py
train_gull_CMaxGrad.py

by SimCLR model

train_butterflies_CMaxGrad_simclr.py
train_gull_CMaxGrad_simclr.py

For the SimCLR, we used repo for training. Thanks for their open-source. For standard model, we trained by

train_butterflies_resnet18.py
train_gull_resnet18_linear.py

For questions, feel free to reach out

Pei Wang: [email protected]

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A Machine Teaching Framework for Scalable Recognition

Related tags

Overview

MEMORABLE

Requirements

Datasets

Implementation details

To generate counterfactual explanations

To generate teaching images and explanations

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