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Found 2 papers, 1 papers with code
$k$-Mixup Regularization for Deep Learning via Optimal Transport
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, i. e. interpolation under the Wasserstein metric.
k-Mixup Regularization for Deep Learning via Optimal Transport
Our empirical results show that training with $k$-mixup further improves generalization and robustness across several network architectures and benchmark datasets of differing modalities.
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