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Anomaly Detection
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection
Joint Detection of Fraud and Concept Drift inOnline Conversations with LLM-Assisted Judgment
Multi-Stage Prompt Inference Attacks on Enterprise LLM Systems
A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys
3DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clustering
A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy
Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection
Adversarial Activation Patching: A Framework for Detecting and Mitigating Emergent Deception in Safety-Aligned Transformers
Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects
Hyperspectral Anomaly Detection Methods: A Survey and Comparative Study
What ZTF Saw Where Rubin Looked: Anomaly Hunting in DR23
seMCD: Sequentially implemented Monte Carlo depth computation with statistical guarantees
Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detection
Evaluating Language Models For Threat Detection in IoT Security Logs
Generative Adversarial Evasion and Out-of-Distribution Detection for UAV Cyber-Attacks
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models
Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection
FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection
Process mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systems
SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale
BrokenVideos: A Benchmark Dataset for Fine-Grained Artifact Localization in AI-Generated Videos
Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series
Joint attitude estimation and 3D neural reconstruction of non-cooperative space objects
E-ABIN: an Explainable module for Anomaly detection in BIological Networks
Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology
Behavioral Anomaly Detection in Distributed Systems via Federated Contrastive Learning
Experimental Assessment of Neural 3D Reconstruction for Small UAV-based Applications
Exact Matrix Seriation through Mathematical Optimization: Stress and Effectiveness-Based Models
Sequential keypoint density estimator: an overlooked baseline of skeleton-based video anomaly detection
Trustworthy Prediction with Gaussian Process Knowledge Scores
Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017
TAB: Unified Benchmarking of Time Series Anomaly Detection Methods
Quantum-Hybrid Support Vector Machines for Anomaly Detection in Industrial Control Systems
Searching for a Hidden Markov Anomaly over Multiple Processes
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