How do you handle the issue of catastrophic forgetting in continual learning?

Catastrophic forgetting is a phenomenon in machine learning where a model forgets how to perform previous tasks when learning new ones, leading to a drop in performance. Continual learning, which involves training a model on a stream of tasks over time, is particularly susceptible to this issue. There are several strategies that can be used to mitigate catastrophic forgetting:

• Regularization: Adding a penalty term to the loss function that encourages the model to retain information from previous tasks can help prevent catastrophic forgetting.
• Rehearsal: Storing a small set of examples from previous tasks and using them to periodically train the model can help it retain information.
• Distillation: Using the predictions of a previously trained model to guide the learning of a new one can help prevent catastrophic forgetting.
• Dynamic Architectures: Using a dynamic architecture that can grow or shrink to accommodate new tasks can help prevent catastrophic forgetting.

It is worth noting that no single strategy is a silver bullet for preventing catastrophic forgetting, and a combination of strategies may be necessary. Additionally, continual learning is an active area of research, and new strategies are constantly being developed.