Catastrophic Forgetting Prevention

Published:

Catastrophic forgetting prevention is used when a model is updated many times and starts losing older skills as it learns new data. The problem is easy to miss at first. The latest update may look like an improvement, while performance quietly drops on cases that the model used to handle well. This often shows up in continual learning and repeated fine-tuning, where the same model parameters are adjusted again and again.

The goal is to keep earlier knowledge from being overwritten during updates. Teams often do this by mixing in older examples while training so the model is reminded of past behavior. Another approach is to separate new learning from old knowledge so updates don’t erase what already works.

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