{"total":53,"rows":[{"id":"2206.09426","title":"ADBench: Anomaly Detection Benchmark","created":"2022-06-19","cited_by_count":232,"area":null},{"id":"2103.00113","title":"Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning","created":"2021-02-27","cited_by_count":177,"area":null},{"id":"2403.06495","title":"Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts","created":"2024-03-11","cited_by_count":89,"area":null},{"id":"2108.00462","title":"Explainable Deep Few-shot Anomaly Detection with Deviation Networks","created":"2021-08-01","cited_by_count":69,"area":null},{"id":"2104.14535","title":"A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection","created":"2021-04-29","cited_by_count":60,"area":null},{"id":"2401.06091","title":"A Closer Look at AUROC and AUPRC under Class Imbalance","created":"2024-01-11","cited_by_count":57,"area":null},{"id":"2105.10500","title":"Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection","created":"2021-05-22","cited_by_count":42,"area":null},{"id":"2409.09957","title":"Deep Graph Anomaly Detection: A Survey and New Perspectives","created":"2024-09-16","cited_by_count":40,"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":"2310.12790","title":"Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection","created":"2023-10-19","cited_by_count":25,"area":null},{"id":"2302.04549","title":"Weakly Supervised Anomaly Detection: A Survey","created":"2023-02-09","cited_by_count":22,"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":"2507.21160","title":"Handling Out-of-Distribution Data: A Survey","created":"2025-07-25","cited_by_count":14,"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":"2311.06835","title":"Open-Set Graph Anomaly Detection via Normal Structure Regularisation","created":"2023-11-12","cited_by_count":11,"area":null},{"id":"2306.14403","title":"Anomaly Detection with Score Distribution Discrimination","created":"2023-06-26","cited_by_count":7,"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":"2302.14696","title":"Dissolving Is Amplifying: Towards Fine-Grained Anomaly Detection","created":"2023-02-28","cited_by_count":3,"area":null},{"id":"2603.01713","title":"Dual Distillation for Few-Shot Anomaly Detection","created":"2026-03-02","cited_by_count":3,"area":null},{"id":"2303.03925","title":"Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption","created":"2023-03-02","cited_by_count":2,"area":null},{"id":"2306.12627","title":"Targeted collapse regularized autoencoder for anomaly detection: black hole at the center","created":"2023-06-22","cited_by_count":2,"area":null},{"id":"2309.07461","title":"Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection","created":"2023-09-14","cited_by_count":2,"area":null},{"id":"2312.04546","title":"Adversarial Learning for Feature Shift Detection and Correction","created":"2023-12-07","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":"2305.18798","title":"AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies","created":"2023-05-30","cited_by_count":1,"area":null},{"id":"2308.10055","title":"Robust Fraud Detection via Supervised Contrastive Learning","created":"2023-08-19","cited_by_count":1,"area":null},{"id":"2312.12010","title":"Outlier detection using flexible categorisation and interrogative agendas","created":"2023-12-19","cited_by_count":1,"area":null},{"id":"2408.14498","title":"Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection","created":"2024-08-23","cited_by_count":1,"area":null},{"id":"2408.15012","title":"Flexible categorization using formal concept analysis and Dempster-Shafer theory","created":"2024-08-23","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":"2507.12295","title":"Text-ADBench: Text Anomaly Detection Benchmark Based on LLM Embeddings","created":"2025-07-16","cited_by_count":1,"area":null},{"id":"2109.07295","title":"New Perspective on Progressive GANs Distillation for One-class Novelty Detection","created":"2021-09-15","cited_by_count":0,"area":null},{"id":"2304.04211","title":"AGAD: Adversarial Generative Anomaly Detection","created":"2023-04-09","cited_by_count":0,"area":null},{"id":"2305.18389","title":"AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling","created":"2023-05-28","cited_by_count":0,"area":null},{"id":"2308.14838","title":"Tackling Diverse Minorities in Imbalanced Classification","created":"2023-08-28","cited_by_count":0,"area":null},{"id":"2406.09147","title":"Weakly-supervised anomaly detection for multimodal data distributions","created":"2024-06-13","cited_by_count":0,"area":null},{"id":"2409.20208","title":"Constraining Anomaly Detection with Anomaly-Free Regions","created":"2024-09-30","cited_by_count":0,"area":null},{"id":"2503.13562","title":"Detecting Scarce and Sparse Anomalous: Solving Dual Imbalance in Multi-Instance Learning","created":"2025-03-17","cited_by_count":0,"area":null},{"id":"2504.14250","title":"Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection","created":"2025-04-19","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":"2507.23712","title":"Anomalous Samples for Few-Shot Anomaly Detection","created":"2025-07-31","cited_by_count":0,"area":null},{"id":"2510.02014","title":"Normality Calibration in Semi-supervised Graph Anomaly Detection","created":"2025-10-02","cited_by_count":0,"area":null},{"id":"2510.13205","title":"CleverCatch: A Knowledge-Guided Weak Supervision Model for Fraud Detection","created":"2025-10-15","cited_by_count":0,"area":null},{"id":"2511.12388","title":"CEDL: Centre-Enhanced Discriminative Learning for Anomaly Detection","created":"2025-11-15","cited_by_count":0,"area":null},{"id":"2512.18774","title":"Label-Informed Outlier Detection Based on Granule Density","created":"2025-12-21","cited_by_count":0,"area":null},{"id":"2512.18977","title":"Consistency-guided semi-supervised outlier detection in heterogeneous data using fuzzy rough sets","created":"2025-12-22","cited_by_count":0,"area":null},{"id":"2512.18978","title":"Outlier detection in mixed-attribute data: a semi-supervised approach with fuzzy approximations and relative entropy","created":"2025-12-22","cited_by_count":0,"area":null},{"id":"2603.29183","title":"IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection","created":"2026-03-31","cited_by_count":0,"area":null},{"id":"2604.25334","title":"VAE-Inf: A statistically interpretable generative paradigm for imbalanced classification","created":"2026-04-28","cited_by_count":0,"area":null},{"id":"2605.11749","title":"Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2605.26068","title":"Rethinking Weak Supervision in Anomaly Detection: A Comprehensive Benchmark","created":"2026-05-25","cited_by_count":0,"area":null}],"next_cursor":null}