Continual Learning

Prevent Catastrophic Forgetting in Continual Learning

Updating a neural network with new data sounds straightforward: continue training on the new examples and deploy the improved model. The difficulty is that an update which helps the new data can damage behavior the model learned earlier. A classifier that learns a new group of products, for example, may become worse at recognizing older groups even though those old classes never changed. This failure is called catastrophic forgetting. It is especially important in continual learning, where a model learns from a sequence of tasks or data distributions instead of training once on a fixed mixed dataset.