mlsanityData to 7 Oct 2026

Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advances

arXiv 2409.11884 ↗First posted 18 Sep 2024cs.LGcited by 24

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Abstract (arXiv)

Out-of-distribution (OOD) detection aims to detect test samples outside the training category space, which is an essential component in building reliable machine learning systems. Existing reviews on OOD detection primarily focus on method taxonomy, surveying the field by categorizing various approaches. However, many recent works concentrate on non-traditional OOD detection scenarios, such as test-time adaptation, multi-modal data sources and other novel contexts. In this survey, we uniquely review recent advances in OOD detection from the task-oriented perspective for the first time. According to the user's access to the model, that is, whether the OOD detection method is allowed to modify or retrain the model, we classify the methods as training-driven or training-agnostic. Besides, considering the rapid development of pre-trained models, large pre-trained model-based OOD detection is also regarded as an important category and discussed separately. Furthermore, we provide a discussion of the evaluation scenarios, a variety of applications, and several future research directions. We believe this survey with new taxonomy will benefit the proposal of new methods and the expansion of more practical scenarios. A curated list of related papers is provided in the Github repository: https://github.com/shuolucs/Awesome-Out-Of-Distribution-Detection.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 197 of its 333 references that are in the map. First posted on arXiv 18 Sep 2024.

Builds on

197 of 333 references.

References · 197

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Citing papers per year: 2025: 11; 2026: 13.

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  1. 2025Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoningcited by 10
  2. 2026Rare Event Analysis of Large Language Modelscited by 6
  3. 2026WorldCoder-Bench: Benchmarking Physically Grounded 3D World Synthesiscited by 6
  4. 2025Leveraging Intermediate Representations for Better Out-of-Distribution Detectioncited by 5
  5. 2025Judge Q: Trainable Queries for Optimized Information Retention in KV Cache Evictioncited by 4
  6. 2025Digital Twin-based Out-of-Distribution Detection in Autonomous Vesselscited by 3
  7. 2026Responsible Agentic AI Requires Explicit Provenancecited by 3
  8. 2025Beyond Boundaries: Leveraging Vision Foundation Models for Source-Free Object Detectioncited by 2
  9. 2026TRACE: A Self-Evolving Skill Bank for Consistent, Limit-Aware LLM Agentscited by 2
  10. 2025Prior Distribution and Model Confidencecited by 1
  11. 2026Dual Distribution Estimation for Zero-shot Noisy Test-Time Adaptation with VLMscited by 1
  12. 2025Improving Out-of-Distribution Detection with Markov Logic Networkscited by 0
12 more
  1. 2025Bag of Coins: A Statistical Probe into Neural Confidence Structurescited by 0
  2. 2025Frustratingly Easy Feature Reconstruction for Out-of-Distribution Detectioncited by 0
  3. 2025Perspectives on a Reliability Monitoring Framework for Agentic AI Systemscited by 0
  4. 2025General OOD Detection via Model-aware and Subspace-aware Variable Prioritycited by 0
  5. 2026Out-of-Distribution Object Detection in Street Scenes via Synthetic Outlier Exposure and Transfer Learningcited by 0
  6. 2026Bounding Box Anomaly Scoring for simple and efficient Out-of-Distribution detectioncited by 0
  7. 2026Backbone-Equated Diffusion OOD via Sparse Internal Snapshotscited by 0
  8. 2026Domain Restriction via Multi SAE Layer Transitionscited by 0
  9. 2026GP-Adapter: Gaussian Process CLIP-Adapter for Few-Shot Out-of-Distribution Detectioncited by 0
  10. 2026MM++: Post-Hoc Scale-Invariant Multilayer OOD Detection via Top-K Gated Feature Fusioncited by 0
  11. 2026VeriCam: A Verification Baseline for the Classification of Unknown Datacited by 0
  12. 2026DR-LabStack: Design and Implementation of a Clinician-Facing Web System for Diabetic Retinopathy Predictioncited by 0