Greenewald, KristjanGu, AnmingChien, Edward2022-07-212022-07-21K. Greenewald, A. Gu, E. Chien. "$k$-Mixup Regularization for Deep Learning via Optimal Transport."https://hdl.handle.net/2144/44915Mixup is a popular regularization technique for training deep neural networks that can improve generalization and increase adversarial robustness. It perturbs input training data in the direction of other randomly-chosen instances in the training set. To better leverage the structure of the data, we extend mixup to k- mixup by perturbing k-batches of training points in the direction of other k-batches using displacement interpolation, interpolation under the Wasserstein metric. We demonstrate theoretically and in simulations that k-mixup preserves cluster and manifold structures, and we extend theory studying efficacy of standard mixup. Our empirical results show that training with k-mixup further improves generalization and robustness on benchmark datasets.en-USThis version of the work is distributed under a Creative Commons Attribution 4.0 license.http://creativecommons.org/licenses/by/4.0/Article734186