3 papers
cs.DC2025
FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection
Silin Chen, Andy Liu, Kangjian Di +4
Unsupervised image anomaly detection (UAD) has become a critical process in industrial and medical applications, but it faces growing challenges due to increasing concerns over dat…
cs.CV2024
Texture-AD: An Anomaly Detection Dataset and Benchmark for Real Algorithm Development
Tianwu Lei, Bohan Wang, Silin Chen +2
Anomaly detection is a crucial process in industrial manufacturing and has made significant advancements recently. However, there is a large variance between the data used in the d…
cs.CV2024
Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection
Tianwu Lei, Silin Chen, Bohan Wang +2
Most unsupervised anomaly detection methods based on representations of normal samples to distinguish anomalies have recently made remarkable progress. However, existing methods on…