Pith. sign in

REVIEW 11 cited by

OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2306.09301 v5 pith:WYGVSUWY submitted 2023-06-15 cs.LG cs.CV

Jingyang Zhang , Jingkang Yang , Pengyun Wang , Haoqi Wang , Yueqian Lin , Haoran Zhang , Yiyou Sun , Xuefeng Du
show 4 more authors
Yixuan Li Ziwei Liu Yiran Chen Hai Li
This is my paper · ORCID
classification cs.LGcs.CV
keywords detectionevaluationopenoodbenchmarkcomprehensivemethodologiesout-of-distributionscope
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world intelligent systems. Despite the emergence of an increasing number of OOD detection methods, the evaluation inconsistencies present challenges for tracking the progress in this field. OpenOOD v1 initiated the unification of the OOD detection evaluation but faced limitations in scalability and scope. In response, this paper presents OpenOOD v1.5, a significant improvement from its predecessor that ensures accurate and standardized evaluation of OOD detection methodologies at large scale. Notably, OpenOOD v1.5 extends its evaluation capabilities to large-scale data sets (ImageNet) and foundation models (e.g., CLIP and DINOv2), and expands its scope to investigate full-spectrum OOD detection which considers semantic and covariate distribution shifts at the same time. This work also contributes in-depth analysis and insights derived from comprehensive experimental results, thereby enriching the knowledge pool of OOD detection methodologies. With these enhancements, OpenOOD v1.5 aims to drive advancements and offer a more robust and comprehensive evaluation benchmark for OOD detection research.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 19 citations worldwide. Full citation record

  1. Activation Subspaces for Out-of-Distribution Detection

    cs.LG 2025-08 conditional novelty 7.0 of 10

    ActSub decomposes activations via SVD of the classifier head into decisive and insignificant subspaces, using cosine similarity on the insignificant part for far-OOD and shaped energy on the decisive part for near-OOD...

  2. Level, Sharpness, and Corpus: Why Zero-Shot OOD Detector Rankings Do Not Transfer

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Zero-shot OOD detector rankings do not transfer across domains or models; a complementary-evidence wrapper (CEG) cuts FPR95 without using OOD samples.

  3. Self-Poisoning in Adaptive Out-of-Distribution Detection: A Sharp-Threshold Theory and Certified Label-Free Calibration

    cs.LG 2026-07 conditional novelty 6.0 of 10

    In adaptive OOD detection, bank impurity follows a mean-field urn law whose kernel slope acts as a reproduction number; a frozen-reserve gate removes the supercritical collapse, and a two-world theorem caps label-free...

  4. Evaluating Epistemic Uncertainty: Beyond OOD Detection and Active Learning

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Epistemic uncertainty should be judged by how well it ranks reducible error, and a new Pareto-gap diagnostic shows proxy-task rankings can invert.

  5. Synthesizing Near-Boundary OOD Samples for Out-of-Distribution Detection

    cs.CV 2025-07 conditional novelty 6.0 of 10

    SynOOD generates synthetic near-boundary OOD images with MLLM-guided inpainting and energy-score gradients, then fine-tunes CLIP image and text features, reporting state-of-the-art OOD detection on ImageNet benchmarks.

  6. DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection

    cs.LG 2025-06 reject novelty 6.0 of 10

    DynaSubVAE proposes a dynamic, non-parametric GMM-style clustering inside a VAE for adaptive OOD detection, but the paper's description contains internal inconsistencies that undermine the stated method.

  7. Balanced Hyperbolic Embeddings Are Natural Out-of-Distribution Detectors

    cs.LG 2025-06 conditional novelty 6.0 of 10

    Norm-balanced, hierarchy-aware hyperbolic prototypes as the classification head improve out-of-distribution detection across many scoring functions and benchmarks.

  8. $\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization

    cs.CV 2025-10 reject novelty 5.0 of 10

    ΔEnergy, an energy-change OOD score for CLIP, and its EBM fine-tuning loss simultaneously improve OOD detection and covariate-shift generalization.

  9. DCV-ROOD Evaluation Framework: Dual Cross-Validation for Robust Out-of-Distribution Detection

    cs.LG 2025-09 conditional novelty 5.0 of 10

    DCV-ROOD is a dual cross-validation framework for OOD detection that splits ID data by stratified folds and OOD data by class groups, reproducing benchmark statistical comparisons at lower cost.

  10. Frustratingly Easy Feature Reconstruction for Out-of-Distribution Detection

    cs.CV 2025-09 conditional novelty 5.0 of 10

    ClaFR computes OOD scores by projecting features onto the top singular subspace of the classifier's weights, eliminating the need for training data access.

  11. Multi-Method Ensemble for Out-of-Distribution Detection

    cs.CV 2025-08 conditional novelty 5.0 of 10

    MME, a product of SCALE, VRA, fDBD, PCA, ViM, NME+ and CO+ scores, shows state-of-the-art OOD detection on common benchmarks, with a theoretical guarantee that is weaker than claimed.

Pith tools