Catastrophic forgetting (or catastrophic interference) is a problem in machine learning where a model forgets an existing learned pattern when learning a new one.

The model uses the same parameters to recognize both patterns, and learning the second pattern overwrites the parameters’ configuration from having learned the first pattern.