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- W4385557982 abstract "Abstract With the rapid development of machine learning today, deep learning has shown great advantages in many fields. Due to the high cost of datasets with labels, its branch semi-supervised learning has become an important research area. However, existing semi-supervised learning methods perform poorly on extremely small datasets, for instance, only one labeled example for each class. Hence, we introduce methods that apply manifold approaches to DL(Deep Learning) and frameworks that implement the idea. Potential application scenarios were analyzed based on experiments on synthetic and real-world experiments." @default.
- W4385557982 created "2023-08-04" @default.
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- W4385557982 date "2023-07-01" @default.
- W4385557982 modified "2023-10-16" @default.
- W4385557982 title "Two Applications of Manifold Regularization in Deep Learning Architectures" @default.
- W4385557982 cites W176175254 @default.
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- W4385557982 doi "https://doi.org/10.1088/1742-6596/2547/1/012004" @default.
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