Task Agnostic Continual Learning Using Online Variational Bayes with Fixed-Point Updates

Understand

Background: Catastrophic forgetting is the notorious vulnerability of neural networks to the changes in the data distribution during learning.

• This phenomenon has long been considered a major obstacle for using learning agents in realistic continual learning settings.
• A large body of continual learning research assumes that task boundaries are known during training.
• However, only a few works consider scenarios in which task boundaries are unknown or not well defined -- task agnostic scenarios.