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Improving out-of-distribution detection by enforcing confidence margin

journal contribution
posted on 2025-04-07, 22:58 authored by L Tamang, Mohamed Reda BouadjenekMohamed Reda Bouadjenek, Richard DazeleyRichard Dazeley, Sunil AryalSunil Aryal
Abstract In many critical machine learning applications, such as autonomous driving and medical image diagnosis, the detection of out-of-distribution (OOD) samples is as crucial as accurately classifying in-distribution (ID) inputs. Recently, outlier exposure (OE)-based methods have shown promising results in detecting OOD inputs via model fine-tuning with auxiliary outlier data. However, most of the previous OE-based approaches emphasize more on synthesizing extra outlier samples or introducing regularization to diversify OOD sample space, which is rather unquantifiable in practice. In this work, we propose a novel and straightforward method called Margin-bounded Confidence Scores (MaCS) to address the nontrivial OOD detection problem by enlarging the disparity between ID and OOD scores, which in turn makes the decision boundary more compact facilitating effective segregation with a simple threshold. Specifically, we augment the learning objective of an OE regularized classifier with a supplementary constraint, which penalizes high confidence scores for OOD inputs compared to that of ID and significantly enhances the OOD detection performance while maintaining the ID classification accuracy. Extensive experiments on various benchmark datasets for image classification tasks demonstrate the effectiveness of the proposed method by significantly outperforming state-of-the-art methods on various benchmarking metrics. The code is publicly available at https://github.com/lakpa-tamang9/margin_ood/tree/kais

History

Journal

Knowledge and Information Systems

Pagination

1-29

Location

Berlin, Germany

ISSN

0219-1377

eISSN

0219-3116

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Publisher

Springer

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