CAAP: Class-Dependent Automatic Data Augmentation Based On Adaptive Policies For Time Series

arXiv:2404.00898v1 Announce Type: new
Abstract: Data Augmentation is a common technique used to enhance the performance of deep learning models by expanding the training dataset. Automatic Data Augmentation (ADA) methods are getting popular because of their capacity to generate policies for various datasets. However, existing ADA methods primarily focused on overall performance improvement, neglecting the problem of class-dependent bias that leads to performance reduction in specific classes. This bias poses significant challenges when deploying models in real-world applications. Furthermore, ADA …

abstract ada arxiv augmentation capacity class cs.lg data dataset datasets deep learning generate however performance policies popular series time series training type