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arXiv AI By Jae-Ho Lee, Min-Yeong Park, Jun-Yeong Moon, Jung Uk Kim, Gyeong-Moon Park

Online Versatile Incremental Learning: Towards Class and Domain-Agnostic Adaptation at Any Time

Read the original on arXiv AI →

The paper introduces Online Versatile Incremental Learning (Online VIL), a scenario where both class concepts and visual domains evolve simultaneously without explicit boundaries. To address this challenge, the authors propose TopFlow, a framework that combines Domain‑agnostic Flow Matching (DFM) and Global Topology Preservation (GTP) to maintain domain‑agnostic representations and the global structure of the feature space without storing past examples. Experiments show that TopFlow outperforms existing methods in the Online VIL setting, offering a promising direction for building continual learning systems in realistic dynamic environments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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