Denoising in Representation Space via Data-Dependent Regularization for Better Representation
Despite the success of deep learning models, it remains challenging for the over-parameterized model to learn good representation under small-sample-size settings. In this paper, motivated by previous work on out-of-distribution (OoD) generalization, we study the representation learning problem from...
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| Hauptverfasser: | , , , |
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| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
MDPI AG
2023-05-01
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| Schriftenreihe: | Mathematics |
| Schlagworte: | |
| Online-Zugang: | https://www.mdpi.com/2227-7390/11/10/2327 |
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