Preprint
Oct 2026
Learning to Curate What You Generate for Generalizable Few-Shot Class-Incremental Learning
This work proposes a framework that curates trustworthy synthetic knowledge for stable G-FSCIL, and designs a boundary-stable incremental adaptation scheme, including synthetic-informed prototype initialization and bidirectional boundary calibration to mitigate old-new conflicts.
Jun-Hui Yin, Yu-Chen Yang, Yi-Lin Yin et al.
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