AI RESEARCH
Multi-level Collaborative Distillation Meets Global Workspace Model: A Unified Framework for OCIL
arXiv CS.LG
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ArXi:2508.08677v2 Announce Type: replace Online Class-Incremental Learning (OCIL) enables models to learn continuously from non-i.i.d. data streams. Since samples of the data streams can be seen only once, it is suitable for real-world scenarios compared to offline learning. However, this constraint intensifies the challenge for OCIL in maintaining an appropriate balance between stability and plasticity. Moreover, under stricter memory buffer constraints in real world, current replay-based methods are less effective.