Out of Distribution (OOD) Detection

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

4 leaderboard tables shown for this task, 4 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

9 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 319 papers with code (629 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Deep Anomaly Detection with Outlier Exposure
  11 Dec 2018 9 repositories listed Syntology ran 5 of 5 samples · 0 unverified · 4 pointer-only (licence)
  We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance.
• Improved Contrastive Divergence Training of Energy Based Models
  2 Dec 2020 6 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  Contrastive divergence is a popular method of training energy-based models, but is known to have difficulties with training stability.
• Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices
  28 Dec 2019 5 repositories listed Syntology ran 5 of 8 samples · 3 unverified · 8 pointer-only (licence)
  We find that characterizing activity patterns by Gram matrices and identifying anomalies in gram matrix values can yield high OOD detection rates.
• GL-MCM: Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution Detection
  10 Apr 2023 4 repositories listed Syntology ran 5 of 16 samples · 11 unverified
  Zero-shot out-of-distribution (OOD) detection is a task that detects OOD images during inference with only in-distribution (ID) class names.
• OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
  13 Oct 2022 4 repositories listed Syntology ran 8 of 17 samples · 9 unverified
  Out-of-distribution (OOD) detection is vital to safety-critical machine learning applications and has thus been extensively studied, with a plethora of methods developed in the literature.
• Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
  12 Apr 2022 4 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  We apply preference modeling and reinforcement learning from human feedback (RLHF) to finetune language models to act as helpful and harmless assistants.
• Generalized Out-of-Distribution Detection: A Survey
  21 Oct 2021 4 repositories listed
  In this survey, we first present a unified framework called generalized OOD detection, which encompasses the five aforementioned problems, i.
• A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
  16 Jun 2021 4 repositories listed
  Mahalanobis distance (MD) is a simple and popular post-processing method for detecting out-of-distribution (OOD) inputs in neural networks.
• Hierarchical VAEs Know What They Don't Know
  16 Feb 2021 4 repositories listed Syntology ran 8 of 10 samples · 2 unverified · 9 pointer-only (licence)
  Deep generative models have been demonstrated as state-of-the-art density estimators.
• Likelihood Ratios for Out-of-Distribution Detection
  7 Jun 2019 4 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  We propose a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics.
• OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection
  15 Jun 2023 3 repositories listed
  Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world intelligent systems.
• A Multi-Head Model for Continual Learning via Out-of-Distribution Replay
  20 Aug 2022 3 repositories listed
  Instead of using the saved samples in memory to update the network for previous tasks/classes in the existing approach, MORE leverages the saved samples to build a task specific classifier (adding a new classification…
• Out of Distribution Detection via Neural Network Anchoring
  8 Jul 2022 3 repositories listed
  Our goal in this paper is to exploit heteroscedastic temperature scaling as a calibration strategy for out of distribution (OOD) detection.
• MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks
  2 Mar 2022 3 repositories listed
  However, disentangling the different types and sources of uncertainty is non trivial for most datasets, especially since there is no ground truth for uncertainty.
• SSD: A Unified Framework for Self-Supervised Outlier Detection
  22 Mar 2021 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  We demonstrate that SSD outperforms most existing detectors based on unlabeled data by a large margin.
• Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and Segmentation
  22 May 2025 2 repositories listed Syntology ran 3 of 4 samples · 1 unverified · 4 pointer-only (licence)
  Out-of-distribution (OOD) detection and segmentation are crucial for deploying machine learning models in safety-critical applications such as autonomous driving and robot-assisted surgery.
• Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selections and Approximations
  21 May 2025 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Grounded on this perspective, we exploit the deviations of InD and OoD features in such a non-linear subspace for effective OoD detection.
• A Benchmark and Evaluation for Real-World Out-of-Distribution Detection Using Vision-Language Models
  30 Jan 2025 2 repositories listed Syntology ran 3 of 7 samples · 4 unverified
  Our experiments reveal that recent CLIP-based OOD detection methods struggle to varying degrees across the three proposed benchmarks, and none of them consistently outperforms the others.
• DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution Detection
  12 Nov 2024 2 repositories listed Syntology ran 3 of 4 samples · 1 unverified · 4 pointer-only (licence)
  Extensive experiments on two tasks, five datasets, and nine base OOD algorithms demonstrate that DPU significantly improves OOD detection performance, setting a new state-of-the-art in multimodal OOD detection, with…
• LAPT: Label-driven Automated Prompt Tuning for OOD Detection with Vision-Language Models
  12 Jul 2024 2 repositories listed Syntology ran 8 of 17 samples · 9 unverified
  The LAPT framework operates autonomously, requiring only ID class names as input and eliminating the need for manual intervention.
• Negative Label Guided OOD Detection with Pretrained Vision-Language Models
  29 Mar 2024 2 repositories listed Syntology ran 9 of 9 samples · 0 unverified · 1 pointer-only (licence)
  In this paper, we propose a novel post hoc OOD detection method, called NegLabel, which takes a vast number of negative labels from extensive corpus databases.
• Kernel PCA for Out-of-Distribution Detection
  5 Feb 2024 2 repositories listed Syntology ran 4 of 5 samples · 1 unverified · 5 pointer-only (licence)
  Out-of-Distribution (OoD) detection is vital for the reliability of Deep Neural Networks (DNNs).
• Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly
  3 Jul 2023 2 repositories listed
  Machine Learning (ML) models in Robotic Assembly Sequence Planning (RASP) need to be introspective on the predicted solutions, i.
• Beyond AUROC & co. for evaluating out-of-distribution detection performance
  26 Jun 2023 2 repositories listed
  While there has been a growing research interest in developing out-of-distribution (OOD) detection methods, there has been comparably little discussion around how these methods should be evaluated.
• LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning
  2 Jun 2023 2 repositories listed Syntology ran 5 of 12 samples · 7 unverified
  CLIP's local features have a lot of ID-irrelevant nuisances (e.
• In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation
  1 Jun 2023 2 repositories listed Syntology ran 1 of 19 samples · 18 unverified
  The OOD detection performance when the in-distribution (ID) is ImageNet-1K is commonly being tested on a small range of test OOD datasets.
• Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection by Distorting Task-Agnostic Features
  30 Jan 2023 2 repositories listed
  We find that: (1) no existing method behaves well in both settings; (2) fine-tuning PLMs on in-distribution data benefits detecting semantic shifts but severely deteriorates detecting non-semantic shifts, which can be…
• Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
  26 Nov 2022 2 repositories listed Syntology ran 3 of 10 samples · 7 unverified
  Semantic segmentation models classify pixels into a set of known (``in-distribution'') visual classes.
• Back to the Basics: Revisiting Out-of-Distribution Detection Baselines
  7 Jul 2022 2 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  We study simple methods for out-of-distribution (OOD) image detection that are compatible with any already trained classifier, relying on only its predictions or learned representations.
• POEM: Out-of-Distribution Detection with Posterior Sampling
  28 Jun 2022 2 repositories listed Syntology ran 5 of 8 samples · 3 unverified · 2 pointer-only (licence)
  In this work, we propose a novel posterior sampling-based outlier mining framework, POEM, which facilitates efficient use of outlier data and promotes learning a compact decision boundary between ID and OOD data for…

Syntology lines on 21 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.