Towards Learning to Remember in Meta Learning of Sequential Domains

Zhenyi Wang, Tiehang Duan, Donglin Zhan, Changyou Chen

Keywords: Meta learning, Continual Learning, Sequential Domain Learning

Abstract:

Meta-learning has made rapid progress in past years, with recent extensions made to avoid catastrophic forgetting in the learning process, namely continual meta learning. It is desirable to generalize the meta learner’s ability to continuously learn in sequential domains, which is largely unexplored to-date. We found through extensive empirical verification that significant improvement
is needed for current continual learning techniques to be applied in the sequential domain meta learning setting. To tackle the problem, we adapt existing dynamic learning rate adaptation techniques to meta learn both model parameters and learning rates. Adaptation on parameters ensures good generalization performance, while adaptation on learning rates is made to avoid
catastrophic forgetting of past domains. Extensive experiments on a sequence of commonly used real-domain data demonstrate the effectiveness of our proposed method, outperforming current strong baselines in continual learning. Our code is made publicly available online (anonymous)

One-sentence Summary: First work to investigate learning to remember in meta learning of *sequential domains*, achieving state of the art compared with existing continual learning techniques.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Reviewed Version (pdf): https://openreview.net/references/pdf?id=C6Ll16GFDo