Intra-Class Mixup for Out-of-Distribution Detection

Intra-Class Mixup for Out-of-Distribution Detection

Deep neural networks (DNNs) have found widespread adoption in solving image recognition and natural language processing tasks. However, they make confident mispredictions when presented with data that does not belong to the training distribution, i.e. out-of-distribution (OoD) samples. Research has...

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Bibliographic Details
Main Authors: Deepak Ravikumar, Sangamesh Kodge, Isha Garg, Kaushik Roy
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Deep learning
deep neural nets
mixup
intra-class mixup
out-of-distribution detection
angular margin
Online Access:https://ieeexplore.ieee.org/document/10064300/

Internet

https://ieeexplore.ieee.org/document/10064300/

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