论文标题
防止当地的歧管入侵：本地混合
Preventing Manifold Intrusion with Locality: Local Mixup
论文作者
论文摘要
混音是一种依赖数据的正则化技术，包括线性插值输入样本和相关输出。已证明，当使用标准机器学习数据集训练时，它可以提高准确性。但是，作者指出，混合物可以产生分布的虚拟样本，甚至可以在增强训练集中产生矛盾，从而可能导致对抗性效应。在本文中，我们介绍了本地混合，其中计算损失时遥远的输入样品会加权。在受限制的设置中，我们证明本地混合可以在偏见和差异之间产生权衡，极端情况下减少了香草培训和古典混合。使用标准化的计算机视觉基准测试，我们还表明本地混合可以提高测试准确性。
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.
