OpenMix+: Revisiting Data Augmentation for Open Set Recognition

Jiang, Guosong; Zhu, Pengfei; Wang, Yu; Hu, Qinghua

doi:10.1109/tcsvt.2023.3268680

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B Inductive Methods2

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Cited by 12 publications

(2 citation statements)

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“…(ii) As for the second group that introduces unknownclass information into model training, the existing DNNbased OSR methods can be roughly divided into two types according to different sources of the unknown-class information: methods that exploit unknown-class information from known-class samples [23,27,45,46,48,49], and methods that introduce unknown-class information from outlier-class samples [43,44].…”

Section: B Inductive Methodsmentioning

confidence: 99%

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A study on open set recognition methods

Sun¹

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Ling Keck-Voon. He constantly encouraged and supported me throughout my Ph.D. career. He has helped me a lot in how to do academic research, in-depth thinking, and manuscript writing. I would like to thank Professor Lin Guoshen for his patient guidance and for leading me into the wonderful world of deep learning. Also, I would like to thank Nanyang Technological University, National Research Foundation Singapore, and SMRT Corporation for funding my education and research.

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Section: B Inductive Methodsmentioning

confidence: 99%

“…Similar to [23], they generated unknown-class features by manifold mixup, while the known-class features could be extracted from the training images. Jiang et al [49] generated high-quality negative images by mixing them, which was proved to reduce both closed space structural risk and open space risk.…”

Section: B Inductive Methodsmentioning

confidence: 99%