{"total":241,"rows":[{"id":"2204.06507","title":"Out-of-Distribution Detection with Deep Nearest Neighbors","created":"2022-04-13","cited_by_count":600,"area":null},{"id":"2205.09310","title":"Mitigating Neural Network Overconfidence with Logit Normalization","created":"2022-05-19","cited_by_count":252,"area":null},{"id":"2206.09426","title":"ADBench: Anomaly Detection Benchmark","created":"2022-06-19","cited_by_count":232,"area":null},{"id":"2211.13445","title":"Delving into Out-of-Distribution Detection with Vision-Language Representations","created":"2022-11-24","cited_by_count":216,"area":null},{"id":"2111.09805","title":"DICE: Leveraging Sparsification for Out-of-Distribution Detection","created":"2021-11-18","cited_by_count":166,"area":null},{"id":"2110.14051","title":"A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges","created":"2021-10-26","cited_by_count":154,"area":null},{"id":"2303.02966","title":"Non-Parametric Outlier Synthesis","created":"2023-03-06","cited_by_count":129,"area":null},{"id":"2203.04450","title":"How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection?","created":"2022-03-08","cited_by_count":128,"area":null},{"id":"2206.13687","title":"POEM: Out-of-Distribution Detection with Posterior Sampling","created":"2022-06-28","cited_by_count":108,"area":null},{"id":"2203.03800","title":"Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild","created":"2022-03-08","cited_by_count":70,"area":null},{"id":"2309.14888","title":"Nearest Neighbor Guidance for Out-of-Distribution Detection","created":"2023-09-26","cited_by_count":64,"area":null},{"id":"2108.11035","title":"NGC: A Unified Framework for Learning with Open-World Noisy Data","created":"2021-08-25","cited_by_count":58,"area":null},{"id":"2411.04125","title":"Community Forensics: Using Thousands of Generators to Train Fake Image Detectors","created":"2024-11-06","cited_by_count":50,"area":null},{"id":"2312.11536","title":"Fast Decision Boundary based Out-of-Distribution Detector","created":"2023-12-15","cited_by_count":49,"area":null},{"id":"2405.16771","title":"ARC: A Generalist Graph Anomaly Detector with In-Context Learning","created":"2024-05-27","cited_by_count":48,"area":null},{"id":"2211.04208","title":"GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection","created":"2022-11-08","cited_by_count":47,"area":null},{"id":"2302.02615","title":"Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need","created":"2023-02-06","cited_by_count":46,"area":null},{"id":"2303.05033","title":"Out-of-distribution Detection with Implicit Outlier Transformation","created":"2023-03-09","cited_by_count":43,"area":null},{"id":"2402.02653","title":"Learning with Mixture of Prototypes for Out-of-Distribution Detection","created":"2024-02-05","cited_by_count":43,"area":null},{"id":"2310.13923","title":"Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation","created":"2023-10-21","cited_by_count":38,"area":null},{"id":"2311.01796","title":"Learning to Augment Distributions for Out-of-Distribution Detection","created":"2023-11-03","cited_by_count":38,"area":null},{"id":"2402.03502","title":"How Does Unlabeled Data Provably Help Out-of-Distribution Detection?","created":"2024-02-05","cited_by_count":36,"area":null},{"id":"2311.01479","title":"Detecting Out-of-Distribution Through the Lens of Neural Collapse","created":"2023-11-02","cited_by_count":34,"area":null},{"id":"2208.02764","title":"OpenCon: Open-world Contrastive Learning","created":"2022-08-04","cited_by_count":33,"area":null},{"id":"2406.00806","title":"Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection","created":"2024-06-02","cited_by_count":32,"area":null},{"id":"2505.18032","title":"Mahalanobis++: Improving OOD Detection via Feature Normalization","created":"2025-05-23","cited_by_count":32,"area":null},{"id":"2311.03236","title":"Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources","created":"2023-11-06","cited_by_count":31,"area":null},{"id":"2212.02295","title":"Block Selection Method for Using Feature Norm in Out-of-distribution Detection","created":"2022-12-05","cited_by_count":30,"area":null},{"id":"2308.06714","title":"Learning on Graphs with Out-of-Distribution Nodes","created":"2023-08-13","cited_by_count":29,"area":null},{"id":"2207.02158","title":"Class-Specific Semantic Reconstruction for Open Set Recognition","created":"2022-07-05","cited_by_count":28,"area":null},{"id":"2306.02879","title":"Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization","created":"2023-06-05","cited_by_count":28,"area":null},{"id":"2107.08976","title":"OODformer: Out-Of-Distribution Detection Transformer","created":"2021-07-19","cited_by_count":27,"area":null},{"id":"2310.05316","title":"Understanding the Feature Norm for Out-of-Distribution Detection","created":"2023-10-09","cited_by_count":27,"area":null},{"id":"2409.11884","title":"Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advances","created":"2024-09-18","cited_by_count":26,"area":null},{"id":"2106.04823","title":"Taxonomy of Machine Learning Safety: A Survey and Primer","created":"2021-06-09","cited_by_count":25,"area":null},{"id":"2210.07805","title":"Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning","created":"2022-10-13","cited_by_count":25,"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":"2306.08751","title":"Improving Selective Visual Question Answering by Learning from Your Peers","created":"2023-06-14","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":"2402.02949","title":"Kernel PCA for Out-of-Distribution Detection","created":"2024-02-05","cited_by_count":24,"area":null},{"id":"2502.13308","title":"A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection","created":"2025-02-18","cited_by_count":24,"area":null},{"id":"2308.10239","title":"From Global to Local: Multi-scale Out-of-distribution Detection","created":"2023-08-20","cited_by_count":18,"area":null},{"id":"2406.15523","title":"Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark","created":"2024-06-21","cited_by_count":18,"area":null},{"id":"2303.00198","title":"Convolutional Visual Prompt for Robust Visual Perception","created":"2023-03-01","cited_by_count":17,"area":null},{"id":"2310.14586","title":"GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels","created":"2023-10-23","cited_by_count":17,"area":null},{"id":"2401.06176","title":"GOODAT: Towards Test-time Graph Out-of-Distribution Detection","created":"2024-01-10","cited_by_count":17,"area":null},{"id":"2105.09270","title":"Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?","created":"2021-05-19","cited_by_count":16,"area":null},{"id":"2302.04002","title":"The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition","created":"2023-02-08","cited_by_count":16,"area":null},{"id":"2306.03715","title":"Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability","created":"2023-06-06","cited_by_count":16,"area":null},{"id":"2412.01023","title":"Learning Structured Representations with Hyperbolic Embeddings","created":"2024-12-02","cited_by_count":16,"area":null},{"id":"2511.19515","title":"Fewer Tokens, Greater Scaling: Self-Adaptive Visual Bases for Efficient and Expansive Representation Learning","created":"2025-11-24","cited_by_count":16,"area":null},{"id":"2302.11716","title":"VRA: Variational Rectified Activation for Out-of-distribution Detection","created":"2023-02-23","cited_by_count":15,"area":null},{"id":"2305.19598","title":"Towards Semi-supervised Universal Graph Classification","created":"2023-05-31","cited_by_count":15,"area":null},{"id":"2312.14452","title":"How to Overcome Curse-of-Dimensionality for Out-of-Distribution Detection?","created":"2023-12-22","cited_by_count":15,"area":null},{"id":"2209.01501","title":"Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions","created":"2022-09-03","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":"2410.11397","title":"FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection","created":"2024-10-15","cited_by_count":14,"area":null},{"id":"2507.01831","title":"Out-of-Distribution Detection Methods Answer the Wrong Questions","created":"2025-07-02","cited_by_count":14,"area":null},{"id":"2303.12267","title":"AUTO: Adaptive Outlier Optimization for Test-Time OOD Detection","created":"2023-03-22","cited_by_count":13,"area":null},{"id":"2109.13157","title":"Visual Anomaly Detection for Images: A Survey","created":"2021-09-27","cited_by_count":12,"area":null},{"id":"2310.16492","title":"On the Powerfulness of Textual Outlier Exposure for Visual OoD Detection","created":"2023-10-25","cited_by_count":12,"area":null},{"id":"2108.13797","title":"Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings","created":"2021-08-30","cited_by_count":11,"area":null},{"id":"2207.11554","title":"3DOS: Towards 3D Open Set Learning -- Benchmarking and Understanding Semantic Novelty Detection on Point Clouds","created":"2022-07-23","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":"2210.01742","title":"CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning","created":"2022-10-04","cited_by_count":11,"area":null},{"id":"2301.04660","title":"Anomalies, Representations, and Self-Supervision","created":"2023-01-11","cited_by_count":11,"area":null},{"id":"2112.09815","title":"Gradient-based Novelty Detection Boosted by Self-supervised Binary Classification","created":"2021-12-18","cited_by_count":10,"area":null},{"id":"2309.12780","title":"LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition","created":"2023-09-22","cited_by_count":10,"area":null},{"id":"2412.05293","title":"FodFoM: Fake Outlier Data by Foundation Models Creates Stronger Visual Out-of-Distribution Detector","created":"2024-11-22","cited_by_count":10,"area":null},{"id":"2106.08365","title":"Test Sample Accuracy Scales with Training Sample Density in Neural Networks","created":"2021-06-15","cited_by_count":9,"area":null},{"id":"2210.11034","title":"Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble","created":"2022-10-20","cited_by_count":9,"area":null},{"id":"2303.07543","title":"WDiscOOD: Out-of-Distribution Detection via Whitened Linear Discriminant Analysis","created":"2023-03-14","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":"2305.16966","title":"Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection","created":"2023-05-26","cited_by_count":9,"area":null},{"id":"2310.04724","title":"Activate and Reject: Towards Safe Domain Generalization under Category Shift","created":"2023-10-07","cited_by_count":9,"area":null},{"id":"2311.16420","title":"Model-free Test Time Adaptation for Out-Of-Distribution Detection","created":"2023-11-28","cited_by_count":9,"area":null},{"id":"2405.08766","title":"Energy-based Hopfield Boosting for Out-of-Distribution Detection","created":"2024-05-14","cited_by_count":9,"area":null},{"id":"2408.10798","title":"Universal Novelty Detection Through Adaptive Contrastive Learning","created":"2024-08-20","cited_by_count":9,"area":null},{"id":"2207.13083","title":"Task Agnostic and Post-hoc Unseen Distribution Detection","created":"2022-07-26","cited_by_count":8,"area":null},{"id":"2303.13148","title":"Calibrated Out-of-Distribution Detection with a Generic Representation","created":"2023-03-23","cited_by_count":8,"area":null},{"id":"2403.16260","title":"Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble","created":"2024-03-24","cited_by_count":8,"area":null},{"id":"2410.13761","title":"GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning","created":"2024-10-17","cited_by_count":8,"area":null},{"id":"2412.05292","title":"TagFog: Textual Anchor Guidance and Fake Outlier Generation for Visual Out-of-Distribution Detection","created":"2024-11-22","cited_by_count":8,"area":null},{"id":"2412.18256","title":"Robust Semi-Supervised Learning in Open Environments","created":"2024-12-24","cited_by_count":8,"area":null},{"id":"2602.18793","title":"From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection","created":"2026-02-21","cited_by_count":8,"area":null},{"id":"2212.12720","title":"Boosting Out-of-Distribution Detection with Multiple Pre-trained Models","created":"2022-12-24","cited_by_count":7,"area":null},{"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":"2309.09982","title":"Introspective Deep Metric Learning","created":"2023-09-11","cited_by_count":7,"area":null},{"id":"2310.01942","title":"OOD Aware Supervised Contrastive Learning","created":"2023-10-03","cited_by_count":7,"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":"2310.10237","title":"SGOOD: Substructure-enhanced Graph-Level Out-of-Distribution Detection","created":"2023-10-16","cited_by_count":7,"area":null},{"id":"2401.13210","title":"Multitask Active Learning for Graph Anomaly Detection","created":"2024-01-24","cited_by_count":7,"area":null},{"id":"2404.16954","title":"Taming False Positives in Out-of-Distribution Detection with Human Feedback","created":"2024-04-25","cited_by_count":7,"area":null},{"id":"2106.03899","title":"Shifting Transformation Learning for Out-of-Distribution Detection","created":"2021-06-07","cited_by_count":6,"area":null},{"id":"2111.12379","title":"Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks","created":"2021-11-24","cited_by_count":6,"area":null},{"id":"2206.07459","title":"READ: Aggregating Reconstruction Error into Out-of-distribution Detection","created":"2022-06-15","cited_by_count":6,"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":"2401.02611","title":"MOODv2: Masked Image Modeling for Out-of-Distribution Detection","created":"2024-01-05","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":"2411.14049","title":"Out-Of-Distribution Detection with Diversification (Provably)","created":"2024-11-21","cited_by_count":6,"area":null}],"next_cursor":"100"}