Augmenting Imbalanced Time-series Data via Adversarial Perturbation in Latent Space
Proceedings of The 13th Asian Conference on Machine Learning, PMLR 157:1633-1644, 2021.
Success of training deep learning models largely depends on the amount and quality of training data. Although numerous data augmentation techniques have already been pro- posed for certain domains such as computer vision where simple schemes such as rotation and flipping have been shown to be effective, other domains such as time-series data have a relatively smaller set of augmentation techniques readily available. Data imbalance is a phenomenon often observed in real-world data. However, a simple oversampling technique may make a model vulnerable to overfitting, so a proper data augmentation is desired. To tackle these problems, we propose a novel data augmentation method that utilizes the latent vectors of an autoencoder in a novel way. When input data are perturbed in its latent space, their reconstructed data retains properties similar to the original one. In con- trast, adversarial augmentation is a technique to train robust deep neural networks against unforeseen data shifts or corruptions by providing a downstream model with samples that are difficult to predict. Our method adversarially perturbs input data in its latent space so that the augmented data is diverse and conducive to reducing test error of a downstream model. The experimental results demonstrated that our method achieves the right balance, significantly modifying the input data to help generalization while retaining its realism.