{"total":232,"rows":[{"id":"2209.00796","title":"Diffusion Models: A Comprehensive Survey of Methods and Applications","created":"2022-09-02","cited_by_count":1114,"area":"diffusion"},{"id":"2301.07597","title":"How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection","created":"2023-01-18","cited_by_count":481,"area":null},{"id":"2210.07242","title":"OpenOOD: Benchmarking Generalized Out-of-Distribution Detection","created":"2022-10-13","cited_by_count":268,"area":null},{"id":"2306.11876","title":"BMAD: Benchmarks for Medical Anomaly Detection","created":"2023-06-20","cited_by_count":82,"area":null},{"id":"2206.10071","title":"BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs","created":"2022-06-21","cited_by_count":80,"area":null},{"id":"2305.18593","title":"On Diffusion Modeling for Anomaly Detection","created":"2023-05-29","cited_by_count":72,"area":"diffusion"},{"id":"2301.13359","title":"IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing","created":"2023-01-31","cited_by_count":64,"area":null},{"id":"2306.12251","title":"GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection","created":"2023-06-21","cited_by_count":58,"area":null},{"id":"2212.02031","title":"Prototypical Residual Networks for Anomaly Detection and Localization","created":"2022-12-05","cited_by_count":56,"area":null},{"id":"2403.14233","title":"SoftPatch: Unsupervised Anomaly Detection with Noisy Data","created":"2024-03-21","cited_by_count":55,"area":null},{"id":"2504.16109","title":"Representation Learning for Tabular Data: A Comprehensive Survey","created":"2025-04-17","cited_by_count":36,"area":null},{"id":"2308.07815","title":"ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition","created":"2023-08-15","cited_by_count":31,"area":null},{"id":"2405.17419","title":"MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities","created":"2024-05-27","cited_by_count":28,"area":null},{"id":"2412.04867","title":"MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects","created":"2024-12-06","cited_by_count":26,"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":"2406.02263","title":"M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising","created":"2024-06-04","cited_by_count":24,"area":null},{"id":"2302.04549","title":"Weakly Supervised Anomaly Detection: A Survey","created":"2023-02-09","cited_by_count":22,"area":null},{"id":"2305.00735","title":"Unsupervised anomaly detection algorithms on real-world data: how many do we need?","created":"2023-05-01","cited_by_count":20,"area":null},{"id":"2211.01834","title":"Toward Unsupervised Outlier Model Selection","created":"2022-11-03","cited_by_count":19,"area":null},{"id":"2407.12165","title":"Building AI Agents for Autonomous Clouds: Challenges and Design Principles","created":"2024-07-16","cited_by_count":19,"area":"agents"},{"id":"2310.18677","title":"Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach","created":"2023-10-28","cited_by_count":18,"area":null},{"id":"2405.04953","title":"Supervised Anomaly Detection for Complex Industrial Images","created":"2024-05-08","cited_by_count":18,"area":null},{"id":"2412.12154","title":"PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection","created":"2024-12-11","cited_by_count":17,"area":null},{"id":"2503.13195","title":"Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey","created":"2025-03-17","cited_by_count":17,"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":"2208.11290","title":"ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels","created":"2022-08-24","cited_by_count":15,"area":"moe"},{"id":"2304.04661","title":"AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges","created":"2023-04-10","cited_by_count":15,"area":null},{"id":"2210.10487","title":"Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection","created":"2022-10-19","cited_by_count":14,"area":null},{"id":"2311.11961","title":"NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation","created":"2023-11-20","cited_by_count":14,"area":null},{"id":"2302.06430","title":"Deep Orthogonal Hypersphere Compression for Anomaly Detection","created":"2023-02-13","cited_by_count":12,"area":null},{"id":"2208.01819","title":"Adversarial Camouflage for Node Injection Attack on Graphs","created":"2022-08-03","cited_by_count":11,"area":null},{"id":"2305.16202","title":"DP-SGD Without Clipping: The Lipschitz Neural Network Way","created":"2023-05-25","cited_by_count":11,"area":null},{"id":"2306.10534","title":"Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation","created":"2023-06-18","cited_by_count":11,"area":null},{"id":"2307.13239","title":"RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision","created":"2023-07-25","cited_by_count":11,"area":null},{"id":"2309.15376","title":"ADGym: Design Choices for Deep Anomaly Detection","created":"2023-09-27","cited_by_count":11,"area":null},{"id":"2303.01978","title":"Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks","created":"2023-01-26","cited_by_count":10,"area":null},{"id":"2402.03785","title":"Weakly Supervised Anomaly Detection via Knowledge-Data Alignment","created":"2024-02-06","cited_by_count":10,"area":null},{"id":"2403.10802","title":"Anomaly Detection Based on Isolation Mechanisms: A Survey","created":"2024-03-16","cited_by_count":10,"area":null},{"id":"2605.07161","title":"SREGym: A Live Benchmark for AI SRE Agents with High-Fidelity Failure Scenarios","created":"2026-05-08","cited_by_count":10,"area":"agents"},{"id":"2208.12433","title":"Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning","created":"2022-08-26","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":"2310.08577","title":"Visual Data-Type Understanding does not emerge from Scaling Vision-Language Models","created":"2023-10-12","cited_by_count":9,"area":"mllm"},{"id":"2401.04266","title":"Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarking","created":"2024-01-08","cited_by_count":9,"area":null},{"id":"2412.07183","title":"Exploring What Why and How: A Multifaceted Benchmark for Causation Understanding of Video Anomaly","created":"2024-12-10","cited_by_count":9,"area":null},{"id":"2502.11059","title":"ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Models","created":"2025-02-16","cited_by_count":9,"area":"moe"},{"id":"2505.24003","title":"Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecasting","created":"2025-05-29","cited_by_count":9,"area":null},{"id":"2602.03018","title":"From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection","created":"2026-02-03","cited_by_count":9,"area":null},{"id":"2209.04184","title":"Anomaly Detection through Unsupervised Federated Learning","created":"2022-09-09","cited_by_count":8,"area":null},{"id":"2305.17048","title":"Intrinsic Self-Supervision for Data Quality Audits","created":"2023-05-26","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":"2306.01997","title":"UADB: Unsupervised Anomaly Detection Booster","created":"2023-06-03","cited_by_count":8,"area":null},{"id":"2309.16220","title":"Unmasking the Chameleons: A Benchmark for Out-of-Distribution Detection in Medical Tabular Data","created":"2023-09-28","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":"2502.17119","title":"Diffusion and Flow Matching Models for Tabular Data: A Survey","created":"2025-02-24","cited_by_count":8,"area":"diffusion"},{"id":"2506.09368","title":"Anomaly Detection and Generation with Diffusion Models: A Survey","created":"2025-06-11","cited_by_count":8,"area":"diffusion"},{"id":"2301.04257","title":"ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models","created":"2023-01-11","cited_by_count":7,"area":null},{"id":"2306.14403","title":"Anomaly Detection with Score Distribution Discrimination","created":"2023-06-26","cited_by_count":7,"area":null},{"id":"2403.11532","title":"Out-of-Distribution Detection Should Use Conformal Prediction (and Vice-versa?)","created":"2024-03-18","cited_by_count":7,"area":null},{"id":"2412.20870","title":"SoftPatch+: Fully Unsupervised Anomaly Classification and Segmentation","created":"2024-12-30","cited_by_count":7,"area":null},{"id":"2505.24030","title":"From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?","created":"2025-05-29","cited_by_count":7,"area":null},{"id":"2603.14171","title":"TACTIC for Navigating the Unknown: Tabular Anomaly deteCTion via In-Context inference","created":"2026-03-15","cited_by_count":7,"area":null},{"id":"2212.09518","title":"FedTADBench: Federated Time-Series Anomaly Detection Benchmark","created":"2022-12-19","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":"2405.18635","title":"When and How Does In-Distribution Label Help Out-of-Distribution Detection?","created":"2024-05-28","cited_by_count":6,"area":null},{"id":"2510.05903","title":"Kaputt: A Large-Scale Dataset for Visual Defect Detection","created":"2025-10-07","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":"2604.21199","title":"ARFBench: Benchmarking Time Series Question Answering Ability for Software Incident Response","created":"2026-04-23","cited_by_count":6,"area":null},{"id":"2211.00372","title":"Meta-Learning for Unsupervised Outlier Detection with Optimal Transport","created":"2022-11-01","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":"2312.02867","title":"Semi-Supervised Health Index Monitoring with Feature Generation and Fusion","created":"2023-12-05","cited_by_count":5,"area":null},{"id":"2406.11703","title":"Unveiling Multiple Descents in Unsupervised Autoencoders","created":"2024-06-17","cited_by_count":5,"area":null},{"id":"2503.23775","title":"Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies","created":"2025-03-31","cited_by_count":5,"area":null},{"id":"2510.06063","title":"TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis","created":"2025-10-07","cited_by_count":5,"area":null},{"id":"2602.09329","title":"MacrOData: New Benchmarks of Thousands of Datasets for Tabular Outlier Detection","created":"2026-02-10","cited_by_count":5,"area":null},{"id":"2208.11727","title":"Hyperparameter Optimization for Unsupervised Outlier Detection","created":"2022-08-24","cited_by_count":4,"area":null},{"id":"2306.10808","title":"Non-contact Sensing for Anomaly Detection in Wind Turbine Blades: A focus-SVDD with Complex-Valued Auto-Encoder Approach","created":"2023-06-19","cited_by_count":4,"area":null},{"id":"2402.16388","title":"Leave-One-Out-, Bootstrap- and Cross-Conformal Anomaly Detectors","created":"2024-02-26","cited_by_count":4,"area":null},{"id":"2405.18050","title":"Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphs","created":"2024-05-28","cited_by_count":4,"area":null},{"id":"2502.08605","title":"CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection","created":"2025-02-12","cited_by_count":4,"area":null},{"id":"2502.12793","title":"Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport","created":"2025-02-18","cited_by_count":4,"area":null},{"id":"2507.15584","title":"We Need to Rethink Benchmarking in Anomaly Detection","created":"2025-07-21","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":"2603.23205","title":"Between Resolution Collapse and Variance Inflation: Weighted Conformal Anomaly Detection in Low-Data Regimes","created":"2026-03-24","cited_by_count":4,"area":null},{"id":"2303.09989","title":"Finding Competence Regions in Domain Generalization","created":"2023-03-17","cited_by_count":3,"area":null},{"id":"2306.12703","title":"OptIForest: Optimal Isolation Forest for Anomaly Detection","created":"2023-06-22","cited_by_count":3,"area":null},{"id":"2308.10504","title":"Adaptive Thresholding Heuristic for KPI Anomaly Detection","created":"2023-08-21","cited_by_count":3,"area":null},{"id":"2308.15059","title":"OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams","created":"2023-08-29","cited_by_count":3,"area":null},{"id":"2309.08374","title":"Understanding the limitations of self-supervised learning for tabular anomaly detection","created":"2023-09-15","cited_by_count":3,"area":null},{"id":"2407.21263","title":"Outlier Detection in Large Radiological Datasets using UMAP","created":"2024-07-31","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":"2501.02975","title":"Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls","created":"2025-01-06","cited_by_count":3,"area":null},{"id":"2502.06911","title":"Foundation Models for Anomaly Detection: Vision and Challenges","created":"2025-02-10","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":"2508.19641","title":"Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses","created":"2025-08-27","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":"2511.14276","title":"Comparing Task-Agnostic Embedding Models for Tabular Data","created":"2025-11-18","cited_by_count":3,"area":null},{"id":"2608.22808","title":"CatchBench: When Can an Agent Failure Be Caught?","created":"2026-08-24","cited_by_count":3,"area":null},{"id":"2208.01712","title":"No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modeling","created":"2022-08-02","cited_by_count":2,"area":null},{"id":"2212.02645","title":"AIDA: Analytic Isolation and Distance-based Anomaly Detection Algorithm","created":"2022-12-05","cited_by_count":2,"area":null}],"next_cursor":"100"}