arXiv stat.ML
· Papers
Drift and Dependence: Layer-wise Information-Theoretic Bounds for Replay-Based Continual Learning
arXiv:2608.11690v1 Announce Type: cross Abstract: Continual learning must absorb new tasks without erasing old ones, and replay---mixing a small buffer of past examples into current training---is among the most effective remedies for catastrophic forgetting. Yet its generalization behavior is shaped by two coupled effe