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Data augmentation is a technique to improve the generalization ability ofmachine learning methods by increasing the size of the dataset. However, sinceevery augmentation method is not equally effective for every dataset, you needto select an appropriate method carefully. We propose a neural network thatdynamically selects the best combination of data augmentation methods using amutually beneficial gating network and a feature consistency loss. The gatingnetwork is able to control how much of each data augmentation is used for therepresentation within the network. The feature consistency loss gives aconstraint that augmented features from the same input should be in similar. Inexperiments, we demonstrate the effectiveness of the proposed method on the 12largest time-series datasets from 2018 UCR Time Series Archive and reveal therelationships between the data augmentation methods through analysis of theproposed method.

Dynamic Data Augmentation with Gating Networks for Time Series Recognition