BootOOD: Self-Supervised Out-of-Distribution Detection via Synthetic Sample Exposure under Neural Collapse

• URL: http://arxiv.org/abs/2511.13539v1
• Date: Mon, 17 Nov 2025 16:12:31 GMT
• Title: BootOOD: Self-Supervised Out-of-Distribution Detection via Synthetic Sample Exposure under Neural Collapse
• Authors: Yuanchao Wang, Tian Qin, Eduardo Valle, Bruno Abrahao,
• Abstract summary: Out-of-distribution (OOD) detection is critical for deploying image classifiers in safety-sensitive environments.<n>We present BootOOD, a fully self-supervised OOD detection framework that bootstraps exclusively from ID data.<n>We show that BootOOD outperforms prior post-hoc methods, surpasses training-based methods without outlier exposure, and is competitive with state-of-the-art outlier-exposure approaches.
• Score: 16.533348266403515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Out-of-distribution (OOD) detection is critical for deploying image classifiers in safety-sensitive environments, yet existing detectors often struggle when OOD samples are semantically similar to the in-distribution (ID) classes. We present BootOOD, a fully self-supervised OOD detection framework that bootstraps exclusively from ID data and is explicitly designed to handle semantically challenging OOD samples. BootOOD synthesizes pseudo-OOD features through simple transformations of ID representations and leverages Neural Collapse (NC), where ID features cluster tightly around class means with consistent feature norms. Unlike prior approaches that aim to constrain OOD features into subspaces orthogonal to the collapsed ID means, BootOOD introduces a lightweight auxiliary head that performs radius-based classification on feature norms. This design decouples OOD detection from the primary classifier and imposes a relaxed requirement: OOD samples are learned to have smaller feature norms than ID features, which is easier to satisfy when ID and OOD are semantically close. Experiments on CIFAR-10, CIFAR-100, and ImageNet-200 show that BootOOD outperforms prior post-hoc methods, surpasses training-based methods without outlier exposure, and is competitive with state-of-the-art outlier-exposure approaches while maintaining or improving ID accuracy.

Related papers

• Going Beyond Conventional OOD Detection [0.0]
  Out-of-distribution (OOD) detection is critical to ensure the safe deployment of deep learning models in critical applications.<n>We present a unified Approach to Spurimatious, fine-grained, and Conventional OOD Detection (ASCOOD)<n>Our approach effectively mitigates the impact of spurious correlations and encourages capturing fine-grained attributes.
  arXiv  Detail & Related papers  (2024-11-16T13:04:52Z)
• Rethinking the Evaluation of Out-of-Distribution Detection: A Sorites Paradox [70.57120710151105]
  Most existing out-of-distribution (OOD) detection benchmarks classify samples with novel labels as the OOD data. Some marginal OOD samples actually have close semantic contents to the in-distribution (ID) sample, which makes determining the OOD sample a Sorites Paradox. We construct a benchmark named Incremental Shift OOD (IS-OOD) to address the issue.
  arXiv  Detail & Related papers  (2024-06-14T09:27:56Z)
• CLIP-driven Outliers Synthesis for few-shot OOD detection [40.6496321698913]
  Few-shot OOD detection focuses on recognizing out-of-distribution (OOD) images that belong to classes unseen during training. Up to now, a mainstream strategy is based on large-scale vision-language models, such as CLIP. We propose CLIP-driven Outliers Synthesis(CLIP-OS) to overcome the lack of reliable OOD supervision information.
  arXiv  Detail & Related papers  (2024-03-30T11:28:05Z)
• ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection [47.16254775587534]
  We propose a novel OOD detection framework that discovers idlike outliers using CLIP citeDBLP:conf/icml/RadfordKHRGASAM21. Benefiting from the powerful CLIP, we only need a small number of ID samples to learn the prompts of the model. Our method achieves superior few-shot learning performance on various real-world image datasets.
  arXiv  Detail & Related papers  (2023-11-26T09:06:40Z)
• Distilling the Unknown to Unveil Certainty [66.29929319664167]
  Out-of-distribution (OOD) detection is critical for identifying test samples that deviate from in-distribution (ID) data, ensuring network robustness and reliability.<n>This paper presents a flexible framework for OOD knowledge distillation that extracts OOD-sensitive information from a network to develop a binary classifier capable of distinguishing between ID and OOD samples.
  arXiv  Detail & Related papers  (2023-11-14T08:05:02Z)
• Detecting Out-of-Distribution Through the Lens of Neural Collapse [7.04686607977352]
  Out-of-Distribution (OOD) detection is critical for safe deployment.<n>Inspired by the phenomenon of Neural Collapse, we propose a versatile and efficient OOD detection method.
  arXiv  Detail & Related papers  (2023-11-02T05:18:28Z)
• Classifier-head Informed Feature Masking and Prototype-based Logit Smoothing for Out-of-Distribution Detection [27.062465089674763]
  Out-of-distribution (OOD) detection is essential when deploying neural networks in the real world. One main challenge is that neural networks often make overconfident predictions on OOD data. We propose an effective post-hoc OOD detection method based on a new feature masking strategy and a novel logit smoothing strategy.
  arXiv  Detail & Related papers  (2023-10-27T12:42:17Z)
• From Global to Local: Multi-scale Out-of-distribution Detection [129.37607313927458]
  Out-of-distribution (OOD) detection aims to detect "unknown" data whose labels have not been seen during the in-distribution (ID) training process. Recent progress in representation learning gives rise to distance-based OOD detection. We propose Multi-scale OOD DEtection (MODE), a first framework leveraging both global visual information and local region details.
  arXiv  Detail & Related papers  (2023-08-20T11:56:25Z)
• Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning [17.409939628100517]
  We propose a unified framework termed OOD Semantic Pruning (OSP), which aims at pruning OOD semantics out from in-distribution (ID) features. OSP surpasses the previous state-of-the-art by 13.7% on accuracy for ID classification and 5.9% on AUROC for OOD detection on TinyImageNet dataset.
  arXiv  Detail & Related papers  (2023-05-29T15:37:07Z)
• Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need [52.88953913542445]
  We find surprisingly that simply using reconstruction-based methods could boost the performance of OOD detection significantly. We take Masked Image Modeling as a pretext task for our OOD detection framework (MOOD)
  arXiv  Detail & Related papers  (2023-02-06T08:24:41Z)
• Provably Robust Detection of Out-of-distribution Data (almost) for free [124.14121487542613]
  Deep neural networks are known to produce highly overconfident predictions on out-of-distribution (OOD) data. In this paper we propose a novel method where from first principles we combine a certifiable OOD detector with a standard classifier into an OOD aware classifier. In this way we achieve the best of two worlds: certifiably adversarially robust OOD detection, even for OOD samples close to the in-distribution, without loss in prediction accuracy and close to state-of-the-art OOD detection performance for non-manipulated OOD data.
  arXiv  Detail & Related papers  (2021-06-08T11:40:49Z)
• Robust Out-of-distribution Detection for Neural Networks [51.19164318924997]
  We show that existing detection mechanisms can be extremely brittle when evaluating on in-distribution and OOD inputs. We propose an effective algorithm called ALOE, which performs robust training by exposing the model to both adversarially crafted inlier and outlier examples.
  arXiv  Detail & Related papers  (2020-03-21T17:46:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.