Variational continual learning


Type
Conference Object
Change log
Authors
Turner, RE 
Bui, Thang 
Li, Yingzhen 
Cuong, Nguyen 
Abstract

This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks. The framework can suc- cessfully train both deep discriminative models and deep generative models in complex continual learning settings where existing tasks evolve over time and en- tirely new tasks emerge. Experimental results show that VCL outperforms state- of-the-art continual learning methods on a variety of tasks, avoiding catastrophic forgetting in a fully automatic way.

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Journal Title
Conference Name
International Conference on Learning Representations
Journal ISSN
Volume Title
Publisher
Sponsorship
Engineering and Physical Sciences Research Council (EP/L000776/1)
Engineering and Physical Sciences Research Council (EP/M026957/1)
EPSRC grants EP/M0269571 and EP/L000776/1