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Author

Hong-Liang Wang

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Conference Open access Sep 2026

From Neural Collapse to Label-Limited Evolving Streams: Geometry-Constrained Learning Under Dynamic Class Imbalance

A novel framework named Neural collapse Inspired Label-limited Evolving stream learning (NILE), which exploits Neural Collapse geometry to explicitly construct a Simplex Equiangular Tight Frame (ETF) as a fixed classifier, ensuring maximal inter-class separability to guide feature discriminability.

Hong-Liang Wang, Hong-Yuan Liu, Qi-Rui Hao et al. · 0 citations
Book Open access Aug 2026

Topology-Aware Neural Collapse for Generalized Category Discovery on Graphs

TopoNC, a topology-aware neural collapse framework for Graph GCD, is proposed and a Topology-Conditioned Pseudo-Labeling strategy that integrates Sinkhorn-based global balancing, Old-Class Top-K Admission Masking, and Neighborhood-Consensus Screening is designed, to reliably guide feature collapse.

Xi Xu, Zhong Zhang, Hong-Liang Wang et al. · 0 citations
Book Open access Aug 2026

Topology-Aware Neural Collapse for Generalized Category Discovery on Graphs

Learning a stable yet highly discriminative representation space that can simultaneously recognize known categories and discover novel ones from limited labeled data is fundamental to Generalized Category Discovery (GCD) on graphs. Recently, Neural Collapse (NC) theory has emerged as a powerful geometric principle for...

Xu Xi, Zhong Zhang, Hongliang Wang et al. · 0 citations

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