{"total":33,"rows":[{"id":"2310.18961","title":"AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection","created":"2023-10-29","cited_by_count":271,"area":null},{"id":"2404.13671","title":"FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization","created":"2024-04-21","cited_by_count":51,"area":null},{"id":"2303.17093","title":"OpenMix: Exploring Outlier Samples for Misclassification Detection","created":"2023-03-30","cited_by_count":38,"area":null},{"id":"2306.04507","title":"Improving neural network representations using human similarity judgments","created":"2023-06-07","cited_by_count":38,"area":null},{"id":"2410.00320","title":"PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection","created":"2024-10-01","cited_by_count":29,"area":null},{"id":"2409.01980","title":"Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey","created":"2024-09-03","cited_by_count":27,"area":null},{"id":"2307.12450","title":"ProtoFL: Unsupervised Federated Learning via Prototypical Distillation","created":"2023-07-23","cited_by_count":12,"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":"2301.06957","title":"FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks","created":"2023-01-17","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":"2406.14866","title":"AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics","created":"2024-06-21","cited_by_count":8,"area":null},{"id":"2412.04304","title":"Towards Zero-shot 3D Anomaly Localization","created":"2024-12-05","cited_by_count":8,"area":null},{"id":"2501.10067","title":"FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization","created":"2025-01-17","cited_by_count":8,"area":null},{"id":"2310.02821","title":"Improving Vision Anomaly Detection with the Guidance of Language Modality","created":"2023-10-04","cited_by_count":7,"area":null},{"id":"2312.17506","title":"A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1","created":"2023-12-29","cited_by_count":7,"area":null},{"id":"2501.16971","title":"RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples","created":"2025-01-28","cited_by_count":7,"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":"2307.02245","title":"Set Learning for Accurate and Calibrated Models","created":"2023-07-05","cited_by_count":2,"area":null},{"id":"2308.14380","title":"Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities","created":"2023-08-28","cited_by_count":2,"area":null},{"id":"2312.15288","title":"Understanding normalization in contrastive representation learning and out-of-distribution detection","created":"2023-12-23","cited_by_count":2,"area":null},{"id":"2402.14469","title":"Reimagining Anomalies: What If Anomalies Were Normal?","created":"2024-02-22","cited_by_count":2,"area":null},{"id":"2404.17931","title":"Critical Review for One-class Classification: recent advances and the reality behind them","created":"2024-04-27","cited_by_count":2,"area":null},{"id":"2302.07849","title":"Zero-Shot Anomaly Detection via Batch Normalization","created":"2023-02-15","cited_by_count":1,"area":null},{"id":"2307.13069","title":"General-Purpose Multi-Modal OOD Detection Framework","created":"2023-07-24","cited_by_count":1,"area":null},{"id":"2407.17083","title":"When Text and Images Don't Mix: Bias-Correcting Language-Image Similarity Scores for Anomaly Detection","created":"2024-07-24","cited_by_count":1,"area":null},{"id":"2508.21396","title":"PMODE: Theoretically Grounded and Modular Mixture Modeling","created":"2025-08-29","cited_by_count":1,"area":null},{"id":"2602.06031","title":"AP-OOD: Attention Pooling for Out-of-Distribution Detection","created":"2026-02-05","cited_by_count":1,"area":null},{"id":"2303.05828","title":"Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution Detection","created":"2023-03-10","cited_by_count":0,"area":null},{"id":"2405.18848","title":"Two Is Better Than One: Aligned Representation Pairs for Anomaly Detection","created":"2024-05-29","cited_by_count":0,"area":null},{"id":"2406.02327","title":"Iterative Deployment Exposure for Unsupervised Out-of-Distribution Detection","created":"2024-06-04","cited_by_count":0,"area":null},{"id":"2406.10617","title":"Enhancing Anomaly Detection Generalization through Knowledge Exposure: The Dual Effects of Augmentation","created":"2024-06-15","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":"2412.03058","title":"Revisiting Energy-Based Model for Out-of-Distribution Detection","created":"2024-12-04","cited_by_count":0,"area":null}],"next_cursor":null}