OSLNet : Deep Small-Sample Classification with an Orthogonal Softmax Layer
A deep neural network of multiple nonlinear layers forms a large function space, which can easily lead to overfitting when it encounters small-sample data. To mitigate overfitting in small-sample classification, learning more discriminative features from small-sample data is becoming a new trend. To...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - (2020) vom: 06. Mai
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| 1. Verfasser: |
Li, Xiaoxu
(VerfasserIn) |
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| Weitere Verfasser: |
Chang, Dongliang,
Ma, Zhanyu,
Tan, Zheng-Hua,
Xue, Jing-Hao,
Cao, Jie,
Yu, Jingyi,
Guo, Jun |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2020
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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| Schlagworte: | Journal Article |
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