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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