Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation

Understand

Data augmentation methods have been shown to be a fundamental technique to improve generalization in tasks such as image, text and audio classification.

• Recently, automated augmentation methods have led to further improvements on image classification and object detection leading to state-of-the-art performances.
• Nevertheless, little work has been done on time-series data, an area that could greatly benefit from automated data augmentation given the usually limited size of the datasets.
• We present two sample-adaptive automatic weighting schemes for data augmentation: the first learns to weight the contribution of the augmented samples to the loss, and the second method selects a subset of transformations based on the ranking of the predicted training loss.