Reducing catastrophic forgetting with learning on synthetic data
Abstract
Catastrophic forgetting is a problem caused by neural networks' inability to learn data in sequence. After learning two tasks in sequence, performance on the first one drops significantly. This is a serious disadvantage that prevents many deep learning applications to real-life problems where not all object classes are known beforehand; or change in data requires adjustments to the model. To reduce this problem we investigate the use of synthetic data, namely we answer a question: Is it possible to generate such data synthetically which learned in sequence does not result in catastrophic forgetting? We propose a method to generate such data in two-step optimisation process via meta-gradients. Our experimental results on Split-MNIST dataset show that training a model on such synthetic data in sequence does not result in catastrophic forgetting. We also show that our method of generating data is robust to different learning scenarios.
Cite
@article{arxiv.2004.14046,
title = {Reducing catastrophic forgetting with learning on synthetic data},
author = {Wojciech Masarczyk and Ivona Tautkute},
journal= {arXiv preprint arXiv:2004.14046},
year = {2020}
}