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absolute memorization

Absolute memorization is a property in continual and incremental machine learning where a model trained sequentially across distinct phases produces the exact same parameters and predictive results as an equivalent model trained jointly on all historical and present data simultaneously. In standard incremental learning, incorporating new classes or tasks typically causes catastrophic forgetting of prior knowledge unless past training examples are retained and revisited, which increases storage demands and risks data privacy. Models that achieve absolute memorization resolve this trade-off by analytically incorporating new information into the learning state, ensuring complete retention of past knowledge across sequential updates without storing or replaying raw historical data.

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ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection

ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection

Huiping Zhuang, Zhenyu Weng, Hongxin Wei, Renchunzi Xie, Kar-Ann Toh, Zhiping Lin

OrganizationsNanyang Technological UniversitySouth China University of TechnologyYonsei University

Why you should read this

Proposes an analytic class-incremental learning framework that mathematically matches the performance of joint training without storing historical exemplar data, eliminating catastrophic forgetting while protecting data privacy across multi-phase learning tasks.

Class-incremental learning (CIL) learns a classification model with training data of different classes arising progressively. Existing CIL either suffers from serious accuracy loss due to catastrophic forgetting, or invades data privacy by revisiting used exemplars. Inspired by linear learning formulations, we propose an analytic class-incremental learning (ACIL) with absolute memorization of past knowledge while avoiding breaching of data privacy (i.e., without storing historical data). The absolute memorization is demonstrated in the sense that class-incremental learning using ACIL given present data would give identical results to that from its joint-learning counterpart which consumes both present and historical samples. This equality is theoretically validated. Data privacy is ensured since no historical data are involved during the learning process. Empirical validations demonstrate ACIL's competitive accuracy performance with near-identical results for various incremental task settings (e.g., 5-50 phases). This also allows ACIL to outperform the state-of-the-art methods for large-phase scenarios (e.g., 25 and 50 phases).

Added

2026-09-26