11 citations · 21 across the 7 of their papers we have counts for
7 papers
Balanced Anomaly-guided Ego-graph Diffusion Model for Inductive Graph Anomaly Detection
Chunyu Wei, Siyuan He, Yu Wang +7
Graph anomaly detection (GAD) is crucial in applications like fraud detection and cybersecurity. Despite recent advancements using graph neural networks (GNNs), two major challenge…
Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration
Yu Wang, Jingyang Lin, Jingjing Zou +3
Our work reveals a structured shortcoming of the existing mainstream self-supervised learning methods. Whereas self-supervised learning frameworks usually take the prevailing perfe…
A Style and Semantic Memory Mechanism for Domain Generalization
Yang Chen, Yu Wang, Yingwei Pan +3
Mainstream state-of-the-art domain generalization algorithms tend to prioritize the assumption on semantic invariance across domains. Meanwhile, the inherent intra-domain style inv…
Transferrable Contrastive Learning for Visual Domain Adaptation
Yang Chen, Yingwei Pan, Yu Wang +3
Self-supervised learning (SSL) has recently become the favorite among feature learning methodologies. It is therefore appealing for domain adaptation approaches to consider incorpo…
A Low Rank Promoting Prior for Unsupervised Contrastive Learning
Yu Wang, Jingyang Lin, Qi Cai +4
Unsupervised learning is just at a tipping point where it could really take off. Among these approaches, contrastive learning has seen tremendous progress and led to state-of-the-a…
Joint Contrastive Learning with Infinite Possibilities
Qi Cai, Yu Wang, Yingwei Pan +2
This paper explores useful modifications of the recent development in contrastive learning via novel probabilistic modeling. We derive a particular form of contrastive loss named J…