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Mixup refers to interpolation-based data augmentation, originally motivatedas a way to go beyond empirical risk minimization (ERM). Its extensions mostlyfocus on the definition of interpolation and the space (input or feature) whereit takes place, while the augmentation process itself is less studied. In mostmethods, the number of generated examples is limited to the mini-batch size andthe number of examples being interpolated is limited to two (pairs), in theinput space.  We make progress in this direction by introducing MultiMix, which generatesan arbitrarily large number of interpolated examples beyond the mini-batch sizeand interpolates the entire mini-batch in the embedding space. Effectively, wesample on the entire convex hull of the mini-batch rather than along linearsegments between pairs of examples.  On sequence data, we further extend to Dense MultiMix. We densely interpolatefeatures and target labels at each spatial location and also apply the lossdensely. To mitigate the lack of dense labels, we inherit labels from examplesand weight interpolation factors by attention as a measure of confidence.  Overall, we increase the number of loss terms per mini-batch by orders ofmagnitude at little additional cost. This is only possible because ofinterpolating in the embedding space. We empirically show that our solutionsyield significant improvement over state-of-the-art mixup methods on fourdifferent benchmarks, despite interpolation being only linear. By analyzing theembedding space, we show that the classes are more tightly clustered anduniformly spread over the embedding space, thereby explaining the improvedbehavior.

Embedding Space Interpolation Beyond Mini-Batch, Beyond Pairs and Beyond Examples