11 citations · 11 across the 8 of their papers we have counts for
8 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…
Value Iteration Networks with Gated Summarization Module
Jinyu Cai, Jialong Li, Mingyue Zhang +1
In this paper, we address the challenges faced by Value Iteration Networks (VIN) in handling larger input maps and mitigating the impact of accumulated errors caused by increased i…
Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network
Jinyu Cai, Yi Han, Wenzhong Guo +1
In this work, we study the problem of partitioning a set of graphs into different groups such that the graphs in the same group are similar while the graphs in different groups are…
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…
Unsupervised Deep Discriminant Analysis Based Clustering
Jinyu Cai, Wenzhong Guo, Jicong Fan
This work presents an unsupervised deep discriminant analysis for clustering. The method is based on deep neural networks and aims to minimize the intra-cluster discrepancy and max…
Perturbation Learning Based Anomaly Detection
Jinyu Cai, Jicong Fan
This paper presents a simple yet effective method for anomaly detection. The main idea is to learn small perturbations to perturb normal data and learn a classifier to classify the…