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The ability to jointly learn from multiple modalities, such as text, audio,and visual data, is a defining feature of intelligent systems. While there havebeen promising advances in designing neural networks to harness multimodaldata, the enormous success of data augmentation currently remains limited tosingle-modality tasks like image classification. Indeed, it is particularlydifficult to augment each modality while preserving the overall semanticstructure of the data; for example, a caption may no longer be a gooddescription of an image after standard augmentations have been applied, such astranslation. Moreover, it is challenging to specify reasonable transformationsthat are not tailored to a particular modality. In this paper, we introduceLeMDA, Learning Multimodal Data Augmentation, an easy-to-use method thatautomatically learns to jointly augment multimodal data in feature space, withno constraints on the identities of the modalities or the relationship betweenmodalities. We show that LeMDA can (1) profoundly improve the performance ofmultimodal deep learning architectures, (2) apply to combinations of modalitiesthat have not been previously considered, and (3) achieve state-of-the-artresults on a wide range of applications comprised of image, text, and tabulardata.

Learning Multimodal Data Augmentation in Feature Space