Learning Invariant Representation for Continual Learning

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).

Abstract

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.

Requirements

• Python 3.6
• Pytorch 1.2
• torchvision 0.4

Usage

You can use main.py to run our IRCL method on the Split MNIST benchmark.

python main.py

Reference

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},
}