What methods help manage catastrophic forgetting in continual learning for generative AI models

Question

Can you tell me methods to manage catastrophic forgetting continual learning for Generative AI models?

Answer 1

To manage catastrophic forgetting in continual learning for generative AI models, the following methods are effective:

• Elastic Weight Consolidation (EWC)
• Penalizes changes to weights critical to previous tasks.
• Replay Buffer
• Stores samples from previous tasks to retrain alongside new tasks.
• Generative Replay
• Uses a generative model to recreate data from earlier tasks for retraining.
• Progressive Networks
• Adds new sub-networks for new tasks while freezing old ones.
• Knowledge Distillation
• Retains knowledge by minimizing divergence between old and new model outputs

          

The above code uses methods to balance learning new tasks while retaining knowledge of previous tasks. A combination like EWC + Replay Buffer is often used for generative AI.

Hence, by referring to these methods, you can manage catastrophic forgetting in continual learning for generative AI models.