Two papers show how pretrained models bypass the proven infeasibility of optimal class-incremental learning
khademinori · x · 2026-09-25
Framing this with his PhD thesis proving the infeasibility of optimal class-incremental learning, the author highlights two new papers explaining why pretrained models get past that barrier.
- The first shows fine-tuning erodes the generalization margin at a rate that grows with the learning rate.
- The second characterizes in closed form the per-stage offset that L2 regularization induces in sequentially trained cross-entropy heads, and introduces gauge anchoring, a constraint that removes it at negligible cost.
- Both results were checked against exact solves under a pre-registered protocol.
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