Open-set Image Classification
Open-Set Image Classification is an image classification paradigm that, unlike traditional closed-set settings, explicitly accounts for the presence of unknown classes during testing by requiring the model to not only correctly classify known classes but also to effectively reject samples from unseen categories.
55
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3.9K
Citations
241
Authors
88
Institutions
Publications per year
2017–2026
51
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55
Classification-Reconstruction Learning for Open-Set Recognition
Ryota Yoshihashi, Wen Shao, Rei Kawakami et al. · 2019 · 451 citations · Full text
Generative OpenMax for Multi-Class Open Set Classification
Zongyuan Ge, Sergey Demyanov, Rahil Garnavi · 2017 · 427 citations
Conditional Gaussian Distribution Learning for Open Set Recognition
Xin Sun, Zhenning Yang, Chi Zhang et al. · 2020 · 291 citations
Generative-Discriminative Feature Representations for Open-Set Recognition
Pramuditha Perera, Vlad I. Morariu, Rajiv Jain et al. · 2020 · 198 citations
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88

Hunan University
Changsha, China
4 h-index
13 publications
746 citations

National University of Defense Technology
Changsha, China
4 h-index
18 publications
726 citations
Nanyang Technological University
Singapore, Singapore
3 h-index
13 publications
2.2K citations
Tsinghua University
Beijing, China
3 h-index
3 publications
245 citations

University of Pisa
Pisa, Italy
2 h-index
6 publications
138 citations

University of Electronic Science and Technology of China
Chengdu, China
2 h-index
13 publications
351 citations

Nanjing University of Aeronautics and Astronautics
Nanjing, China
2 h-index
5 publications
272 citations

Chinese Academy of Sciences
Beijing, China
2 h-index
8 publications
986 citations
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Repository · 7 publications · 260 citations
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TPAMI · 4 publications · 460 citations
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GRSL · 2 publications · 41 citations
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2 publications · 69 citations
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2 publications · 52 citations
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