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Jun, 2018
数据增强代替显式正则化
Data augmentation instead of explicit regularization
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Alex Hernández-García, Peter König
TL;DR
通过研究比较权值衰减、随机失活和数据增强等正则化技术在深度学习中的作用,提出了数据增强对于提高深度学习泛化性能的显著贡献。因此,建议不要使用权值衰减和随机失活,而要更加关注数据增强和其他归纳偏差来优化神经网络。
Abstract
Modern deep artificial neural networks have achieved impressive results through models with very large capacity---compared to the number of training examples---that control overfitting with the help of different forms of
regularization
.
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