MODALS: Data augmentation that works for everyone

Anonymous

Keywords: deep learning, data augmentation, latent space, data modalities, automated data augmentation

Abstract: The usefulness of data augmentation has led to the development of specific techniques of augmentation unique to each modality of data. The techniques developed for one modality usually suit the type of data in that particular modality. For image data some commonly used augmentation techniques are rotation, cropping, applying affine transforms, random flips, contrast and color augmentations and adding Gaussian blur. More recent techniques like CutMix[3] and MixUp[4] apply data augmentation on both the image and label space. Many robust data augmentation techniques for image data already exist and are used widely. However modalities like tables and graph data don’t have as many robust augmentation techniques. Since the techniques developed for images were made taking into consideration the nature of image data, they usually cannot be applied to other modalities. If a generalized, modality agnostic framework for augmentation could be developed then standard, robust augmentation techniques can be applied across many modalities. This is exactly what the authors of the paper propose.

ICLR Paper: https://openreview.net/forum?id=XjYgR6gbCEc