Randomized neural network with adaptive forward regularization for online task-free class incremental learning.

Abstract

Class incremental learning (CIL) requires an agent to learn distinct tasks consecutively with knowledge retention against forgetting. Problems impeding the practice of CIL methods are twofold: (1) prompt update on non-i.i.d batch streams without boundary, namely the harsher online task-free CIL (OTCIL) scenario; (2) CIL methods suffer from heavy forgetting on learning long task streams, as shown in Fig. 1(a). To achieve efficient decision-making, the ensemble deep random vector functional link network (edRVFL) with forward regularization (-F) is proposed to replace the canonical Ridge (-R), reducing more regrets during OTCIL. Considering continuous distribution drifting on long stream, we further propose edRVFL-kF to adjust the intervention intensity of forward knowledge and derive incremental updates. edRVFL-kF can effectively avoid replay, retraining, and catastrophic forgetting while achieving lower regret over -R. Moreover, to improve robustness on non-i.i.d stream and eliminate intractable tuning of -kF, we rebuild with online Bayesian learning and propose the plug-and-play edRVFL-kF-Bayes, enabling all hard ks in multiple sub-learners to self-adapt to ever-changing distribution and optimization in OTCIL. Experiments were conducted on image datasets, including multiple evaluations, ablation tests, estimated forward, and compatibility studies, which distinctly validate the efficacy of edRVFL-kF-Bayes.