Super(ficial)-alignment: Strong Models May Deceive Weak Models in Weak-to-Strong Generalization

1 code implementation • 17 Jun 2024 • Wenkai Yang, Shiqi Shen, Guangyao Shen, Zhi Gong, Yankai Lin

However, we are concerned that behind such a promising phenomenon, whether there exists an issue of weak-to-strong deception, where strong models may deceive weak models by exhibiting well-aligned in areas known to weak models but producing misaligned behaviors in cases weak models do not know.

SGOOD: Substructure-enhanced Graph-Level Out-of-Distribution Detection

1 code implementation • 16 Oct 2023 • Zhihao Ding, Jieming Shi, Shiqi Shen, Xuequn Shang, Jiannong Cao, Zhipeng Wang, Zhi Gong

We find that substructure differences commonly exist between ID and OOD graphs, and design SGOOD with a series of techniques to encode task-agnostic substructures for effective OOD detection.

Out-of-Distribution Detection Representation Learning

A Survey of Deep Visual Cross-Domain Few-Shot Learning

no code implementations • 16 Mar 2023 • Wenjian Wang, Lijuan Duan, Yuxi Wang, Junsong Fan, Zhi Gong, Zhaoxiang Zhang

Research into Cross-Domain Few-Shot (CDFS) has emerged to address this issue, forming a more challenging and realistic setting.

cross-domain few-shot learning Transfer Learning