What is Catastrophic Forgetting?
Catastrophic forgetting (also called catastrophic interference) is a phenomenon where a neural network, when trained on new data or tasks, loses previously learned knowledge. The new training overwrites the weights that encoded earlier capabilities, causing dramatic performance degradation on the original tasks.
Why It Matters
Catastrophic forgetting is a fundamental challenge for AI systems that need to learn continuously. It explains why you can't simply keep fine-tuning the same model on new data without risk — the model may lose its general capabilities. This is a core concern for continual learning, personalization, and keeping models up-to-date without full retraining.
How It Works
Why it happens:
- Neural networks store knowledge distributed across weights (parameters)
- When trained on new data, gradient updates modify these shared weights
- If the new task's optimal weights differ from the old task's, old knowledge is overwritten
- Smaller models are more susceptible (fewer parameters to share)
When it occurs:
- Fine-tuning — adapting a pre-trained model to a specific task may degrade general knowledge
- Sequential task learning — training on task A, then task B, then task C causes forgetting of A
- Data distribution shift — updating a model with data from a different distribution