A small network lost 12.3 percentage points of accuracy on a task it had already mastered, simply by learning a second one. That is real forgetting, and it still missed the registered threshold for what I called severe. A protection method invented in 2017 recovered 98% of the loss without slowing the new task down.

Key takeaways

Plain sequential fine-tuning dropped Task A accuracy from 97.88% to 85.55%, a 12.3 point loss, below the 20-point bar I registered for "severe" forgetting.

Elastic Weight Consolidation (EWC) recovered 98.0% of that loss, bringing Task A back to 97.63%, while Task B accuracy stayed within half a point of the unprotected run.

The registered gate for severe forgetting fired and rejected that hypothesis; the gate for EWC's effectiveness passed by a wide margin.…