Generalized Variational Continual Learning

• URL: http://arxiv.org/abs/2011.12328v1
• Date: Tue, 24 Nov 2020 19:07:39 GMT
• Title: Generalized Variational Continual Learning
• Authors: Noel Loo, Siddharth Swaroop, Richard E. Turner
• Abstract summary: Two main approaches to continuous learning are Online Elastic Weight Consolidation and Variational Continual Learning. We show that applying this modification to mitigate Online EWC as a limiting case, allowing baselines between the two approaches. In order to the observed overpruning effect of VI, we take inspiration from a common multi-task architecture, mitigate neural networks with task-specific FiLM layers.
• Score: 33.194866396158005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continual learning deals with training models on new tasks and datasets in an online fashion. One strand of research has used probabilistic regularization for continual learning, with two of the main approaches in this vein being Online Elastic Weight Consolidation (Online EWC) and Variational Continual Learning (VCL). VCL employs variational inference, which in other settings has been improved empirically by applying likelihood-tempering. We show that applying this modification to VCL recovers Online EWC as a limiting case, allowing for interpolation between the two approaches. We term the general algorithm Generalized VCL (GVCL). In order to mitigate the observed overpruning effect of VI, we take inspiration from a common multi-task architecture, neural networks with task-specific FiLM layers, and find that this addition leads to significant performance gains, specifically for variational methods. In the small-data regime, GVCL strongly outperforms existing baselines. In larger datasets, GVCL with FiLM layers outperforms or is competitive with existing baselines in terms of accuracy, whilst also providing significantly better calibration.

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