Continual Deep Learning by Functional Regularisation of Memorable PastDownload PDF

12 Jun 2020 (modified: 13 Jul 2020)ICML 2020 Workshop LifelongML Blind SubmissionReaders: Everyone
  • Student First Author: Yes
  • TL;DR: This paper introduces a scalable functional-regularisation approach for continual learning that uses a GP formulation of neural networks to identify and regularise over a memorable past.
  • Keywords: Continual learning, lifelong learning, deep learning, functional regularisation
  • Abstract: Continually learning new skills is important for intelligent systems, yet standard deep learning methods suffer from catastrophic forgetting of the past. Recent works address this with weight regularisation. Functional regularisation, although computationally expensive, is expected to perform better, but rarely does so in practice. In this paper, we fix this issue by using a new functional-regularisation approach that utilises a few memorable past examples crucial to avoid forgetting. By using a Gaussian Process formulation of deep networks, our approach enables training in weight-space while identifying both the memorable past and a functional prior. Our method achieves state-of-the-art performance on standard benchmarks and opens a new direction for life-long learning where regularisation and memory-based methods are naturally combined.
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