Damiano Meier

Asymptotic analysis of shallow and deep forgetting in replay with neural collapse

A persistent paradox in Continual Learning is that neural networks often retain linearly separable representations of past tasks even when their output predictions fail. We formalize this distinction as the gap between deep (feature-space) and shallow (classifier-level) forgetting. We demonstrate th…