Papers › Task Agnostic Continual Learning Using Online Variational Bayes

Task Agnostic Continual Learning Using Online Variational Bayes

27 Mar 2018arXiv:1803.10123archive 2025-07-28

Chen Zeno, Itay Golan, Elad Hoffer, Daniel Soudry

Catastrophic forgetting is the notorious vulnerability of neural networks to the change of the data distribution while learning. This phenomenon has long been considered a major obstacle for allowing the use of learning agents in realistic continual learning settings. A large body of continual learning research assumes that task boundaries are known during training. However, research for scenarios in which task boundaries are unknown during training has been lacking. In this paper we present, for the first time, a method for preventing catastrophic forgetting (BGD) for scenarios with task boundaries that are unknown during training --- task-agnostic continual learning. Code of our algorithm is available at https://github.com/igolan/bgd.

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