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.

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• 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.