Free-Flow Class-Incremental Learning: Towards Robust CIL under Variable Class Arrivals
Class-incremental learning (CIL) is commonly evaluated under predefined schedules with fixed or nearly equal class increments, leaving irregular class-arrival scenarios underexplored. However, practical CIL systems may need to update whenever new categories emerge, without forcing them into balanced task partitions. We formalize this setting as Free-Flow Class-Incremental Learning (FFCIL), where the number of newly arriving classes can vary substantially across learning stages. We show that variable class arrivals alter the class composition of incremental training, the reliability of new-class classifier statistics, and the consistency of representations learned across increments, causin...
| Authors | Zhiming Xu, Baile Xu, Jian Zhao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 3 April 2026 |
| Last updated at source | 17 August 2026 |
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