Mixup is a generic and straightforward data augmentation principle. In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels. By doing so, …

Oct 25, 2017 · By doing so, mixup regularizes the neural network to favor simple linear behavior in-between training examples. Our experiments on the ImageNet-2012, CIFAR-10, CIFAR …

In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels. …

Implementation of the mixup training method. Contribute to hongyi-zhang/mixup development by creating an account on GitHub.

In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels. By doing so, mixup regularizes the neural network to favor simple linear behavior in …

By doing so, mixup regularizes the neural network to favor simple linear behavior in-between training examples. Our experiments on the ImageNet-2012, CIFAR-10, CIFAR-100, Google …

Oct 25, 2017 · In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup trains a neural network on convex combinations of pairs of …

mixup reduces overfitting and improves generalization. The following plots show test error curves of a typical training session using the PreAct ResNet-18 architecture (default; you can make …

Oct 9, 2020 · This explains why models obtained by Mixup training exhibits robustness to several kinds of adversarial attacks such as Fast Gradient Sign Method (FGSM). For generalization, …

Aug 10, 2021 · How can we implement mixup in practice? The fastbook, by Jeremy Howard and Sylvain Gugger, does a great job in simplifying the concept. Essentially, mixup can be …