4 papers
Fix the Loss, Not the Radius: Rethinking the Adversarial Perturbation of Sharpness-Aware Minimization
Jinping Wang, Qinhan Liu, Zhiwu Xie +1
Sharpness-Aware Minimization (SAM) improves generalization by minimizing the worst-case loss within a fixed parameter-space radius neighborhood. SAM and its variants mainly rely on…
Rethinking Loss Reweighting for Imbalance Learning as an Inverse Problem: A Neural Collapse Point of View
Jinping Wang, Zixin Tong, Zhiwu Xie +1
Loss reweighting is a widely used strategy for long-tailed classification, but existing reweighting strategies often rely on heuristics and rarely define a well-specified target. I…
Space Alignment Matters: The Missing Piece for Inducing Neural Collapse in Long-Tailed Learning
Jinping Wang, Zhiqiang Gao, Zhiwu Xie
Recent studies on Neural Collapse (NC) reveal that, under class-balanced conditions, the class feature means and classifier weights spontaneously align into a simplex equiangular t…
Escaping Optimization Stagnation: Taking Steps Beyond Task Arithmetic via Difference Vectors
Jinping Wang, Zhiqiang Gao, Dinggen Zhang +1
Current methods for editing pre-trained models face significant challenges, primarily high computational costs and limited scalability. Task arithmetic has recently emerged as a pr…