🤖 AI Summary
To address catastrophic forgetting in memory-constrained streaming learning, this paper proposes a unified continual learning framework based on state replay, applicable to generative (autoencoding), time-series forecasting, and classification tasks. Unlike naive sequential fine-tuning or black-box replay, we formulate state replay as a joint optimization objective, enabling cooperative parameter updates over new and replayed samples via stochastic gradient methods; theoretical analysis grounded in gradient alignment reveals necessary conditions for effective forgetting mitigation. Evaluated across six heterogeneous and stationary streaming settings—constructed from Rotated MNIST, Electricity, and Airlines datasets—the method reduces average forgetting by 2–3× under heterogeneous multi-task streams, while matching fine-tuning performance on stationary streams. Our key contributions are: (i) the first theoretical analysis framework for state replay that unifies generative and discriminative tasks, and (ii) empirical validation of its effectiveness and robustness as a strong baseline for streaming continual learning.
📝 Abstract
Many deployed learning systems must update models on streaming data under memory constraints. The default strategy, sequential fine-tuning on each new phase, is architecture-agnostic but often suffers catastrophic forgetting when later phases correspond to different sub-populations or tasks. Replay with a finite buffer is a simple alternative, yet its behaviour across generative and predictive objectives is not well understood. We present a unified study of stateful replay for streaming autoencoding, time series forecasting, and classification. We view both sequential fine-tuning and replay as stochastic gradient methods for an ideal joint objective, and use a gradient alignment analysis to show when mixing current and historical samples should reduce forgetting. We then evaluate a single replay mechanism on six streaming scenarios built from Rotated MNIST, ElectricityLoadDiagrams 2011-2014, and Airlines delay data, using matched training budgets and three seeds. On heterogeneous multi task streams, replay reduces average forgetting by a factor of two to three, while on benign time based streams both methods perform similarly. These results position stateful replay as a strong and simple baseline for continual learning in streaming environments.