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Learning from Students: Online Contrastive Distillation Network for General Continual Learning

This repository contains the PyTorch implementation for the IJCAI 2022 Paper "Learning from Students: Online Contrastive Distillation Network for General Continual Learning" (Corresponding author: Prof. Zhong Ji)

Our paper is available at Online

If you have any questions on this paper, feel free to create an issue.

Citation

Please cite our paper if it is helpful to your work:

@inproceedings{ocd_net,
author = {Li, Jin and Ji, Zhong and Wang, Gang and Wang, Qiang and Gao, Feng},
title = {Learning from Students: Online Contrastive Distillation Network for General Continual Learning},
booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence},
pages     = {3215--3221},
year      = {2022},
note      = {Main Track}
doi       = {10.24963/ijcai.2022/446},
}

Acknowledgements

Our implementations use the source code from the following repositories and users:

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ocd-net's Issues

Best_params for tinyimagenet

Hi,
I would like to reproduce TinyImageNet results. But, I do not see best hyperparameters for TimyImageNet. Please update them.
Thanks in advance,

Results not reproducible

Dear authors,

I am trying to reproduce your results on Seq-CIFAR100 with multiple runs with different seeds.
I see some of the issues in your code. The CL model has three heads namely: one for SSL, one for classification and one for rotation prediction. However, the paper does not talk about the rotation prediction. Could you give more intuition how rotation prediction actually helps and how much is the boost coming from such an auxiliary task? Please let me know.

Without the rotation prediction, results drop by quite a lot and are on par with DER++. Please let me know how this can be fixed.

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