mixup: Beyond Empirical Risk Minimization

ICLR 2018 Hongyi ZhangMoustapha CisseYann N. DauphinDavid Lopez-Paz

Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issues... (read more)

PDF Abstract

Evaluation results from the paper

Task Dataset Model Metric name Metric value Global rank Compare
Image Classification CIFAR-10 DenseNet-BC-190 + Mixup Percentage correct 97.3 # 6
Image Classification CIFAR-100 DenseNet-BC-190 + Mixup Percentage correct 83.20 # 6