Geometry-Anchored Transport Framework for Exemplar-Free Class-Incremental Learning
Exemplar-free class-incremental learning (EFCIL) requires stable decision boundaries within a shifting feature space. While maintaining class-conditional Gaussian statistics provides a principled classification strategy, these parametric summaries remain sensitive to anisotropic representation drift. Existing methods often transport these statistics across tasks using a decoupled, post-hoc paradigm: optimizing a backbone without explicit geometric constraints can distort the legacy manifold, limiting the precision of retroactive alignment. In this paper, we formulate feature transport as an endogenous training constraint rather than a separate post-task step, presenting the Geometry-Ancho...
| Authors | Hongye Xu, Bartosz Krawczyk |
|---|---|
| Primary category | cs.LG |
| Published | 24 June 2026 |
| Last updated at source | 25 June 2026 |
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