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Contrastive Training for Improved Out-of-Distribution Detection

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arxiv 2007.05566 v1 pith:WDLASH5T submitted 2020-07-10 cs.LG stat.ML

Jim Winkens , Rudy Bunel , Abhijit Guha Roy , Robert Stanforth , Vivek Natarajan , Joseph R. Ledsam , Patricia MacWilliams , Pushmeet Kohli
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Alan Karthikesalingam Simon Kohl Taylan Cemgil S. M. Ali Eslami Olaf Ronneberger
This is my paper · ORCID
classification cs.LGstat.ML
keywords detectioncontrastiveperformancetrainingmethodsout-of-distributionaccessapproach
verification ladder T0 review T1 audit T2 compute T3 formal
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Reliable detection of out-of-distribution (OOD) inputs is increasingly understood to be a precondition for deployment of machine learning systems. This paper proposes and investigates the use of contrastive training to boost OOD detection performance. Unlike leading methods for OOD detection, our approach does not require access to examples labeled explicitly as OOD, which can be difficult to collect in practice. We show in extensive experiments that contrastive training significantly helps OOD detection performance on a number of common benchmarks. By introducing and employing the Confusion Log Probability (CLP) score, which quantifies the difficulty of the OOD detection task by capturing the similarity of inlier and outlier datasets, we show that our method especially improves performance in the `near OOD' classes -- a particularly challenging setting for previous methods.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 52 citations worldwide. Full citation record

  1. 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.

  2. 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.

  3. 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.

  4. Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning

    cs.CV 2025-06 conditional novelty 5.0 of 10

    Uncertainty-guided LiDAR panoptic segmentation (ULOPS) uses evidential learning and three uncertainty losses to segment unknown objects, outperforming prior open-set baselines on KITTI-360 and nuScenes.

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