4 papers
Reconstruction-Shift Discrimination via Mask-Guided Latent Diffusion for Medical Anomaly Detection
Yibo Wan, Jinyu Cai, Yunhe Zhang +2
Unsupervised medical anomaly detection learns normal anatomical patterns from healthy training images and identifies deviations at test time. Reconstruction-based and diffusion-bas…
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework
Jinyu Cai, Yunhe Zhang, Zhoumin Lu +2
Graph anomaly detection (GAD) aims to identify anomalous graphs that significantly deviate from other ones, which has raised growing attention due to the broad existence and comple…
Self-Discriminative Modeling for Anomalous Graph Detection
Jinyu Cai, Yunhe Zhang, Jicong Fan
This paper studies the problem of detecting anomalous graphs using a machine learning model trained on only normal graphs, which has many applications in molecule, biology, and soc…
Deep Orthogonal Hypersphere Compression for Anomaly Detection
Yunhe Zhang, Yan Sun, Jinyu Cai +1
Many well-known and effective anomaly detection methods assume that a reasonable decision boundary has a hypersphere shape, which however is difficult to obtain in practice and is…