Continual Deep Learning by Functional Regularisation of Memorable Past
- URL: http://arxiv.org/abs/2004.14070v4
- Date: Fri, 8 Jan 2021 09:48:17 GMT
- Title: Continual Deep Learning by Functional Regularisation of Memorable Past
- Authors: Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen,
Richard E. Turner, Mohammad Emtiyaz Khan
- Abstract summary: Continually learning new skills is important for intelligent systems, yet standard deep learning methods suffer from catastrophic forgetting of the past.
We propose a new functional-regularisation approach that utilises a few memorable past examples crucial to avoid forgetting.
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.
- Score: 95.97578574330934
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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