{"total":99,"rows":[{"id":"2206.09426","title":"ADBench: Anomaly Detection Benchmark","created":"2022-06-19","cited_by_count":232,"area":null},{"id":"2202.08088","title":"Latent Outlier Exposure for Anomaly Detection with Contaminated Data","created":"2022-02-16","cited_by_count":48,"area":null},{"id":"2205.13845","title":"Raising the Bar in Graph-level Anomaly Detection","created":"2022-05-27","cited_by_count":45,"area":null},{"id":"2207.12201","title":"Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection","created":"2022-07-25","cited_by_count":40,"area":null},{"id":"2305.16114","title":"Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning","created":"2023-05-25","cited_by_count":25,"area":null},{"id":"2205.05173","title":"Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook","created":"2022-05-10","cited_by_count":24,"area":null},{"id":"2206.02704","title":"Perturbation Learning Based Anomaly Detection","created":"2022-06-06","cited_by_count":24,"area":null},{"id":"2206.07647","title":"Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution","created":"2022-06-15","cited_by_count":24,"area":null},{"id":"2209.14930","title":"Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges","created":"2022-09-29","cited_by_count":23,"area":null},{"id":"2305.15121","title":"Beyond Individual Input for Deep Anomaly Detection on Tabular Data","created":"2023-05-24","cited_by_count":16,"area":null},{"id":"2203.02034","title":"Data-Efficient and Interpretable Tabular Anomaly Detection","created":"2022-03-03","cited_by_count":15,"area":null},{"id":"2210.10773","title":"Anomaly Detection Requires Better Representations","created":"2022-10-19","cited_by_count":15,"area":null},{"id":"2302.12245","title":"Set Features for Fine-grained Anomaly Detection","created":"2023-02-23","cited_by_count":13,"area":null},{"id":"2202.03944","title":"Detecting Anomalies within Time Series using Local Neural Transformations","created":"2022-02-08","cited_by_count":12,"area":null},{"id":"2204.09825","title":"A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms","created":"2022-04-21","cited_by_count":11,"area":null},{"id":"2208.07734","title":"Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success","created":"2022-08-16","cited_by_count":11,"area":null},{"id":"2107.07820","title":"Contrastive Predictive Coding for Anomaly Detection","created":"2021-07-16","cited_by_count":10,"area":null},{"id":"2207.01472","title":"Deep Contrastive One-Class Time Series Anomaly Detection","created":"2022-07-04","cited_by_count":10,"area":null},{"id":"2302.07832","title":"Deep Anomaly Detection under Labeling Budget Constraints","created":"2023-02-15","cited_by_count":9,"area":null},{"id":"2305.13189","title":"Unsupervised Anomaly Detection with Rejection","created":"2023-05-22","cited_by_count":9,"area":null},{"id":"2205.01362","title":"TracInAD: Measuring Influence for Anomaly Detection","created":"2022-05-03","cited_by_count":8,"area":null},{"id":"2306.00582","title":"Anomaly Detection with Variance Stabilized Density Estimation","created":"2023-06-01","cited_by_count":8,"area":null},{"id":"2310.10461","title":"Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data","created":"2023-10-16","cited_by_count":8,"area":null},{"id":"2506.09368","title":"Anomaly Detection and Generation with Diffusion Models: A Survey","created":"2025-06-11","cited_by_count":8,"area":"diffusion"},{"id":"2306.07284","title":"No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection","created":"2023-06-12","cited_by_count":7,"area":null},{"id":"2307.06534","title":"DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection","created":"2023-07-13","cited_by_count":6,"area":null},{"id":"2310.06261","title":"Self-Discriminative Modeling for Anomalous Graph Detection","created":"2023-10-10","cited_by_count":6,"area":null},{"id":"2603.14407","title":"Towards One-for-All Anomaly Detection for Tabular Data","created":"2026-03-15","cited_by_count":6,"area":null},{"id":"2303.05904","title":"Deep Anomaly Detection on Tennessee Eastman Process Data","created":"2023-03-10","cited_by_count":5,"area":null},{"id":"2307.12336","title":"TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models","created":"2023-07-23","cited_by_count":5,"area":"diffusion"},{"id":"2209.11796","title":"Composite Convolution: a Flexible Operator for Deep Learning on 3D Point Clouds","created":"2022-09-23","cited_by_count":4,"area":null},{"id":"2306.12033","title":"Self-Tuning Self-Supervised Image Anomaly Detection","created":"2023-06-21","cited_by_count":4,"area":null},{"id":"2312.13896","title":"Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments","created":"2023-12-21","cited_by_count":4,"area":null},{"id":"2405.12502","title":"EntropyStop: Unsupervised Deep Outlier Detection with Loss Entropy","created":"2024-05-21","cited_by_count":4,"area":null},{"id":"2407.08838","title":"Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation","created":"2024-07-11","cited_by_count":4,"area":null},{"id":"2411.07574","title":"Disentangling Tabular Data Towards Better One-Class Anomaly Detection","created":"2024-11-12","cited_by_count":4,"area":null},{"id":"2501.15196","title":"A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges","created":"2025-01-25","cited_by_count":4,"area":null},{"id":"2503.18385","title":"RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Data","created":"2025-03-24","cited_by_count":4,"area":null},{"id":"2603.19497","title":"ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes","created":"2026-03-19","cited_by_count":4,"area":null},{"id":"2205.04041","title":"Deep Federated Anomaly Detection for Multivariate Time Series Data","created":"2022-05-09","cited_by_count":3,"area":null},{"id":"2210.14056","title":"Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings","created":"2022-10-25","cited_by_count":3,"area":null},{"id":"2311.14773","title":"Set Features for Anomaly Detection","created":"2023-11-24","cited_by_count":3,"area":null},{"id":"2312.01541","title":"Revisiting Non-separable Binary Classification and its Applications in Anomaly Detection","created":"2023-12-03","cited_by_count":3,"area":null},{"id":"2404.17454","title":"Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and Beyond","created":"2024-04-26","cited_by_count":3,"area":null},{"id":"2408.04442","title":"FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data","created":"2024-08-08","cited_by_count":3,"area":null},{"id":"2409.10069","title":"Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies","created":"2024-09-16","cited_by_count":3,"area":null},{"id":"2412.08501","title":"GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through Gradient","created":"2024-12-11","cited_by_count":3,"area":null},{"id":"2504.20733","title":"Unsupervised Surrogate Anomaly Detection","created":"2025-04-29","cited_by_count":3,"area":null},{"id":"2509.06911","title":"Hypergraph-Guided Regex Filter Synthesis for Event-Based Anomaly Detection","created":"2025-09-08","cited_by_count":3,"area":null},{"id":"2510.02060","title":"ReTabAD: A Benchmark for Restoring Semantic Context in Tabular Anomaly Detection","created":"2025-10-02","cited_by_count":3,"area":null},{"id":"2510.21296","title":"An Evidence-Based Post-Hoc Adjustment Framework for Anomaly Detection Under Data Contamination","created":"2025-10-24","cited_by_count":3,"area":null},{"id":"2605.02970","title":"Decompose to Understand, Fuse to Detect: Frequency-Decoupled Anomaly Detection for Encrypted Network Traffic","created":"2026-05-03","cited_by_count":3,"area":null},{"id":"2207.03576","title":"Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems","created":"2022-06-25","cited_by_count":2,"area":null},{"id":"2210.07340","title":"LEAVES: Learning Views for Time-Series Biobehavioral Data in Contrastive Learning","created":"2022-10-13","cited_by_count":2,"area":null},{"id":"2307.04097","title":"Restricted Generative Projection for One-Class Classification and Anomaly Detection","created":"2023-07-09","cited_by_count":2,"area":null},{"id":"2401.00033","title":"Hybrid Modeling Design Patterns","created":"2023-12-29","cited_by_count":2,"area":null},{"id":"2401.00773","title":"Unsupervised Outlier Detection using Random Subspace and Subsampling Ensembles of Dirichlet Process Mixtures","created":"2024-01-01","cited_by_count":2,"area":null},{"id":"2405.13699","title":"Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior","created":"2024-05-22","cited_by_count":2,"area":null},{"id":"2405.18929","title":"Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data","created":"2024-05-29","cited_by_count":2,"area":null},{"id":"2405.20341","title":"From Zero to Hero: Cold-Start Anomaly Detection","created":"2024-05-30","cited_by_count":2,"area":null},{"id":"2502.13833","title":"Contrastive Learning-Based privacy metrics in Tabular Synthetic Datasets","created":"2025-02-19","cited_by_count":2,"area":null},{"id":"2512.01672","title":"ICAD-LLM: One-for-All Anomaly Detection via In-Context Learning with Large Language Models","created":"2025-12-01","cited_by_count":2,"area":null},{"id":"2202.04067","title":"Time Series Anomaly Detection by Cumulative Radon Features","created":"2022-02-08","cited_by_count":1,"area":null},{"id":"2302.07849","title":"Zero-Shot Anomaly Detection via Batch Normalization","created":"2023-02-15","cited_by_count":1,"area":null},{"id":"2304.07898","title":"Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection","created":"2023-04-16","cited_by_count":1,"area":null},{"id":"2306.15266","title":"Generalized Out-of-distribution Fault Diagnosis (GOOFD) via Internal Contrastive Learning","created":"2023-06-27","cited_by_count":1,"area":null},{"id":"2401.11271","title":"DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection","created":"2024-01-20","cited_by_count":1,"area":null},{"id":"2404.03495","title":"Deep Transductive Outlier Detection","created":"2024-04-04","cited_by_count":1,"area":null},{"id":"2408.10885","title":"Low-Quality Image Detection by Hierarchical VAE","created":"2024-08-20","cited_by_count":1,"area":null},{"id":"2410.21322","title":"Angel or Devil: Discriminating Hard Samples and Anomaly Contaminations for Unsupervised Time Series Anomaly Detection","created":"2024-10-26","cited_by_count":1,"area":null},{"id":"2502.16076","title":"Harnessing Feature Resonance under Arbitrary Target Alignment for Out-of-Distribution Node Detection","created":"2025-02-22","cited_by_count":1,"area":null},{"id":"2507.12295","title":"Text-ADBench: Text Anomaly Detection Benchmark Based on LLM Embeddings","created":"2025-07-16","cited_by_count":1,"area":null},{"id":"2508.00909","title":"NeuCoReClass AD: Redefining Self-Supervised Time Series Anomaly Detection","created":"2025-07-29","cited_by_count":1,"area":null},{"id":"2509.09030","title":"Contextual Learning for Anomaly Detection in Tabular Data","created":"2025-09-10","cited_by_count":1,"area":null},{"id":"2602.07798","title":"CausalTAD: Injecting Causal Knowledge into Large Language Models for Tabular Anomaly Detection","created":"2026-02-08","cited_by_count":1,"area":null},{"id":"2605.00936","title":"EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systems","created":"2026-05-01","cited_by_count":1,"area":null},{"id":"2302.05757","title":"Multispectral Contrastive Learning with Viewmaker Networks","created":"2023-02-11","cited_by_count":0,"area":null},{"id":"2305.16777","title":"Unleashing the Potential of Unsupervised Deep Outlier Detection through Automated Training Stopping","created":"2023-05-26","cited_by_count":0,"area":null},{"id":"2307.12301","title":"Image Outlier Detection Without Training using RANSAC","created":"2023-07-23","cited_by_count":0,"area":null},{"id":"2401.10300","title":"A Hierarchical Framework with Spatio-Temporal Consistency Learning for Emergence Detection in Complex Adaptive Systems","created":"2024-01-18","cited_by_count":0,"area":null},{"id":"2404.02865","title":"End-To-End Self-Tuning Self-Supervised Time Series Anomaly Detection","created":"2024-04-03","cited_by_count":0,"area":null},{"id":"2407.05382","title":"Rethinking Unsupervised Outlier Detection via Multiple Thresholding","created":"2024-07-07","cited_by_count":0,"area":null},{"id":"2502.18002","title":"A Radon-Nikod\\'ym Perspective on Anomaly Detection: Theory and Implications","created":"2025-02-25","cited_by_count":0,"area":null},{"id":"2506.02757","title":"Investigating Mask-aware Prototype Learning for Tabular Anomaly Detection","created":"2025-06-03","cited_by_count":0,"area":null},{"id":"2506.13217","title":"Polyra Swarms: A Shape-Based Approach to Machine Learning","created":"2025-06-16","cited_by_count":0,"area":null},{"id":"2506.13955","title":"Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies","created":"2025-06-16","cited_by_count":0,"area":null},{"id":"2507.06624","title":"UniOD: A Universal Model for Outlier Detection across Diverse Domains","created":"2025-07-09","cited_by_count":0,"area":null},{"id":"2602.06810","title":"Calibrating Tabular Anomaly Detection via Optimal Transport","created":"2026-02-06","cited_by_count":0,"area":null},{"id":"2602.09593","title":"Why the Counterintuitive Phenomenon of Likelihood Rarely Appears in Tabular Anomaly Detection with Deep Generative Models?","created":"2026-02-10","cited_by_count":0,"area":null},{"id":"2604.18266","title":"Enhancing Tabular Anomaly Detection via Pseudo-Label-Guided Generation","created":"2026-04-20","cited_by_count":0,"area":null},{"id":"2605.06955","title":"Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.10242","title":"When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection","created":"2026-05-11","cited_by_count":0,"area":null},{"id":"2606.13277","title":"ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterization","created":"2026-06-11","cited_by_count":0,"area":null},{"id":"2606.28970","title":"RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection","created":"2026-06-27","cited_by_count":0,"area":null},{"id":"2606.29791","title":"What Drives the Inlier-Memorization Effect? A Theory of Outlier Detection via Early Training Dynamics","created":"2026-06-29","cited_by_count":0,"area":null},{"id":"2608.08906","title":"Federated Attention Autoencoders with a Stochastic Aggregation Scheme for Anomaly Detection","created":"2026-08-09","cited_by_count":0,"area":null},{"id":"2608.19463","title":"LLM as Detector: An In-context Learning Approach for Tabular Anomaly Detection","created":"2026-08-19","cited_by_count":0,"area":null},{"id":"2608.19801","title":"Unsupervised Anomaly Detection Using Flow Matching on Tabular Data","created":"2026-08-20","cited_by_count":0,"area":null},{"id":"2610.02345","title":"Mitigating Convergence Collapse in Fixed-Target Anomaly Detectors via Kernel-Anchored Locality Regularization","created":"2026-10-01","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}