This is the official PyTorch implementation for the Learning Invariant Representation for Continual Learning paper in Meta-Learning for Computer Vision Workshop at the 35th AAAI Conference on Artificial Intelligence (AAAI-2021).
We propose a new pseudo-rehearsal-based method, named learning Invariant Representation for Continual Learning (IRCL), in which class-invariant representation is disentangled from a conditional generative model and jointly used with class-specific representation to learn the sequential tasks. Disentangling the shared invariant representation helps to learn continually a sequence of tasks, while being more robust to forgetting and having better knowledge transfer. We focus on class incremental learning where there is no knowledge about task identity during inference.
- Python 3.6
- Pytorch 1.2
- torchvision 0.4
You can use main.py to run our IRCL method on the Split MNIST benchmark.
python main.py
If you use this code, please cite our paper:
@inproceedings{sokar2021learning,
title={Learning Invariant Representation for Continual Learning},
author={Ghada Sokar and Decebal Constantin Mocanu and Mykola Pechenizkiy},
booktitle={Meta-Learning for Computer Vision Workshop at the 35th AAAI Conference on Artificial Intelligence (AAAI-21)},
year={2021},
}