On Regularization Properties of Artificial Datasets for Deep Learning

08/19/2019 ∙ by Karol Antczak, et al. ∙ 0

The paper discusses regularization properties of artificial data for deep learning. Artificial datasets allow to train neural networks in the case of a real data shortage. It is demonstrated that the artificial data generation process, described as injecting noise to high-level features, bears several similarities to existing regularization methods for deep neural networks. One can treat this property of artificial data as a kind of "deep" regularization. It is thus possible to regularize hidden layers of the network by generating the training data in a certain way.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.