DMix: Adaptive Distance-aware Interpolative Mixup

Interpolation-based regularisation methods such as Mixup, which generate virtual training samples, have proven to be effective for various tasks and modalities. We extend Mixup and propose DMIX, an adaptive distanceaware interpolative Mixup that selects samples based on their diversity in the embedding space. DMIX leverages the hyperbolic space as a similarity measure among input samples for a richer encoded representation. DMIX achieves state-of-the-art results on sentence classification over existing data augmentation methods on 8 benchmark datasets across English, Arabic, Turkish, and Hindi languages while achieving benchmark F1 scores in 3 times less number of iterations. We probe the effectiveness of DMIX in conjunction with various similarity measures and qualitatively analyze the different components. DMIX being generalizable, can be applied to various tasks, models and modalities.