Preventing Manifold Intrusion with Locality: Local Mixup

Abstract

Mixup is a data-dependent regularization technique that consists in linearly interpolating input samples and associated outputs. It has been shown to improve accuracy when used to train on standard machine learning datasets. However, authors have pointed out that Mixup can produce out-of-distribution virtual samples and even contradictions in the augmented training set, potentially resulting in adversarial effects. In this paper, we introduce Local Mixup in which distant input samples are weighted down when computing the loss. In constrained settings we demonstrate that Local Mixup can create a trade-off between bias and variance, with the extreme cases reducing to vanilla training and classical Mixup. Using standardized computer vision benchmarks , we also show that Local Mixup can improve test accuracy.

C-Mixup: Improving Generalization in Regression

“…Comparisons and Experimental Setups. We compare C-Mixup with mixup and its variants (Manifold mixup [68], k-Mixup [20] and Local Mixup [5]) that can be easily to adapted to regression tasks. We also compare to MixRL, a recent reinforcement learning framework to select mixup pairs in regression.…”