AI RESEARCH
Advancing Analytic Class-Incremental Learning through Vision-Language Calibration
arXiv CS.LG
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ArXi:2602.13670v2 Announce Type: replace Class-incremental learning (CIL) with pre-trained models (PTMs) faces a critical trade-off between efficient adaptation and long-term stability. While analytic learning enables rapid, recursive closed-form updates, its efficacy is often compromised by accumulated errors and feature incompatibility. In this paper, we first conduct a systematic study to dissect the failure modes of PTM-based analytic CIL, identifying representation rigidity as the primary bottleneck.