SOTAVerified

Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

Showing 110 of 4856 papers

TitleStatusHype
Multi-Stage Prompt Inference Attacks on Enterprise LLM Systems0
3DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clustering0
A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys0
A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy0
Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection0
Adversarial Activation Patching: A Framework for Detecting and Mitigating Emergent Deception in Safety-Aligned Transformers0
Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial DefectsCode2
Hyperspectral Anomaly Detection Methods: A Survey and Comparative Study0
seMCD: Sequentially implemented Monte Carlo depth computation with statistical guarantees0
What ZTF Saw Where Rubin Looked: Anomaly Hunting in DR230
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MULDE-object-centric-microAUC94.3Unverified
2AMSRCAUC93.8Unverified
3AI-VADAUC93.7Unverified
4SSMTL++v1AUC93.7Unverified
5Background-Agnostic Framework+SSMCTBAUC93.2Unverified
6SSMTL+UBnormalAUC93Unverified
7Background- Agnostic Framework+SSPCABAUC92.9Unverified
8DMADAUC92.8Unverified
9VideoPatchCoreAUC92.8Unverified
10PGMAUC92.72Unverified