We introduce Noisy Feature Mixup (NFM), an inexpensive yet effective method for data augmentation that combines the best of interpolation based training and noise injection schemes. Rather than training with convex combinations of pairs of examples and their labels, we use noise-perturbed convex combinations of pairs of data points in both input and feature space. This method includes mixup and manifold mixup as special cases, but it has additional advantages, including better smoothing of decision boundaries and enabling improved model robustness. We provide theory to understand this as well as the implicit regularization effects of NFM. Our theory is supported by empirical results, demonstrating the advantage of NFM, as compared to mixup and manifold mixup. We show that residual networks and vision transformers trained with NFM have favorable trade-offs between predictive accuracy on clean data and robustness with respect to various types of data perturbation across a range of computer vision benchmark datasets.

我们介绍了一种名为 NFM 的廉价且有效的数据增强方法，它将基于插值的训练和噪声注入方案结合起来，可获得对决策边界的更好平滑和更好的模型健壮性。我们提供了理论支持和实验证据，表明相对于 mixup 和 Manifold mixup，采用 NFM 得到的模型在多种数据扰动和计算机视觉基准数据集下，具有干净数据的预测准确性和鲁棒性之间的有利平衡。

有噪声的特征混合