Skipout: An Adaptive Layer-Level Regularization Framework for Deep Neural Networks
Regularization methods can surprisingly improve the generalization ability of deep neural networks. Among numerous methods, the branch of Dropout regularization is very popular in practice. However, Dropout-like regularization variants have some deficiencies: blindness of regularization, dependency...
Tallennettuna:
| Päätekijät: | , |
|---|---|
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
IEEE
2022-01-01
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| Sarja: | IEEE Access |
| Aiheet: | |
| Linkit: | https://ieeexplore.ieee.org/document/9782436/ |
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