SOTAVerified

Supervised Anomaly Detection

In the training set, the amount of abnormal samples is limited and significant fewer than normal samples, producing data distributions that lead to a naturally imbalanced learning problem.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–110 of 155 papers

TitleStatusHype
LesionPaste: One-Shot Anomaly Detection for Medical Images—0
Diffusion Models for Medical Anomaly DetectionCode1
Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning—0
Anomaly Detection in File Fragment Classification of Image File Formats—0
Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos—0
Self-Supervised Anomaly Detection by Self-Distillation and Negative SamplingCode0
SLA^2P: Self-supervised Anomaly Detection with Adversarial PerturbationCode1
A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods—0
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and LocalizationCode1
No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection—0
Show:102550
← PrevPage 11 of 16Next →

No leaderboard results yet.