{"title":"FLaNS: Feature-Label Negative Sampling for Out-of-Distribution Detection","authors":"Chaejin Lim;Junhee Hyeon;Kiseong Lee;Dongil Han","doi":"10.1109/ACCESS.2025.3548702","DOIUrl":null,"url":null,"abstract":"Many existing deep learning models suffer from a fundamental limitation, where they often misclassify Out-of-Distribution (OOD) data as In-Distribution (ID) data. OOD data represent data patterns that differ from the training distribution, such as images of unseen classes or different domains. This misclassification occurs because deep learning models are inherently designed to classify inputs into one of their known categories. To address this limitation, we propose Feature-Label Negative Sampling (FLaNS), which exploits the observation that OOD data inherently exhibit mismatches between features and their assigned labels. Our method constructs negative data by deliberately creating feature-label mismatches from ID data, which naturally deviate from the learned ID distribution. These synthetic data enable the Support Vector Machine (SVM) to learn decision boundaries that discriminate between ID and OOD data without requiring actual OOD data during training. We evaluated our method on the NCT-CRC-HE and CIFAR-100 datasets using two backbone models. Our approach demonstrates stable and reliable performance in both Receiver Operating Characteristics (ROC) Curve Area (AUROC) and False Positive Rate at 95% True Positive Rate (FPR95) metrics compared to existing methods. This research enhances OOD detection by introducing a method that leverages feature-label mismatches in ID data, improving detection performance without needing diverse OOD data, which are often difficult to collect.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"43878-43888"},"PeriodicalIF":3.4000,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10915589","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Access","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10915589/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Many existing deep learning models suffer from a fundamental limitation, where they often misclassify Out-of-Distribution (OOD) data as In-Distribution (ID) data. OOD data represent data patterns that differ from the training distribution, such as images of unseen classes or different domains. This misclassification occurs because deep learning models are inherently designed to classify inputs into one of their known categories. To address this limitation, we propose Feature-Label Negative Sampling (FLaNS), which exploits the observation that OOD data inherently exhibit mismatches between features and their assigned labels. Our method constructs negative data by deliberately creating feature-label mismatches from ID data, which naturally deviate from the learned ID distribution. These synthetic data enable the Support Vector Machine (SVM) to learn decision boundaries that discriminate between ID and OOD data without requiring actual OOD data during training. We evaluated our method on the NCT-CRC-HE and CIFAR-100 datasets using two backbone models. Our approach demonstrates stable and reliable performance in both Receiver Operating Characteristics (ROC) Curve Area (AUROC) and False Positive Rate at 95% True Positive Rate (FPR95) metrics compared to existing methods. This research enhances OOD detection by introducing a method that leverages feature-label mismatches in ID data, improving detection performance without needing diverse OOD data, which are often difficult to collect.
IEEE AccessCOMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍:
IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest.
IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on:
Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals.
Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering.
Development of new or improved fabrication or manufacturing techniques.
Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.