Drift and Dependence: Layer-wise Information-Theoretic Bounds for Replay-Based Continual Learning
This work decomposes the expected generalization gap into a replay-induced representation drift and an optimization-dependence term, the latter further resolved into stability, plasticity, interaction, and residual-coupling components and develops a layer-wise information-theoretic framework that separates these effects at every depth.