Exploring distribution-based approaches for out-of-distribution detection in deep learning models

Detecting unknown samples is a crucial task for deep learning applications, especially when considering open-set problems such as autonomous driving or disease classification. To improve DL models’ robustness in identifying unseen classes, out-of-distribution (OOD) methods are utilized to distinguis...

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Vydané v:	Neural computing & applications Ročník 37; číslo 17; s. 10807 - 10822
Hlavní autori:	Carvalho, Thiago, Vellasco, Marley, Amaral, José Franco
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.06.2025 Springer Nature B.V
Predmet:	Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Deep learning Image Processing and Computer Vision Probabilistic models Probability and Statistics in Computer Science S.I.: Timely Advances of Deep Learning with applications and Data Driven Modeling Special Issue on Timely Advances of Deep Learning with applications and Data Driven Modeling Out-of-distribution detection Gaussian distribution Dirichlet Process Mixture Deep learning application
ISSN:	0941-0643, 1433-3058
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