Enhancing Out-of-Distribution Detection Under Covariate Shifts: A Full-Spectrum Contrastive Denoisin...
Enhancing Out-of-Distribution Detection Under Covariate Shifts: A Full-Spectrum Contrastive Denoising Framework
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Basel: MDPI AG
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English
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Basel: MDPI AG
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Out-of-distribution (OOD) detection is crucial for identifying samples that deviate from the training distribution, thereby enhancing the reliability of deep neural network models. However, existing OOD detection methods primarily address semantic shifts, where an image’s inherent semantics have changed, and often overlook covariate shifts, which a...
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Enhancing Out-of-Distribution Detection Under Covariate Shifts: A Full-Spectrum Contrastive Denoising Framework
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TN_cdi_proquest_journals_3203195678
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_3203195678
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ISSN
2079-9292
E-ISSN
2079-9292
DOI
10.3390/electronics14091881
