  Regional dropout strategies have been proposed to enhance the performance of
convolutional neural network classifiers. They have proved to be effective for
guiding the model to attend on less discriminative parts of objects (e.g. leg
as opposed to head of a person), thereby letting the network generalize better
and have better object localization capabilities. On the other hand, current
methods for regional dropout remove informative pixels on training images by
overlaying a patch of either black pixels or random noise. Such removal is not
desirable because it leads to information loss and inefficiency during
training. We therefore propose the CutMix augmentation strategy: patches are
cut and pasted among training images where the ground truth labels are also
mixed proportionally to the area of the patches. By making efficient use of
training pixels and retaining the regularization effect of regional dropout,
CutMix consistently outperforms the state-of-the-art augmentation strategies on
CIFAR and ImageNet classification tasks, as well as on the ImageNet
weakly-supervised localization task. Moreover, unlike previous augmentation
methods, our CutMix-trained ImageNet classifier, when used as a pretrained
model, results in consistent performance gains in Pascal detection and MS-COCO
image captioning benchmarks. We also show that CutMix improves the model
robustness against input corruptions and its out-of-distribution detection
performances. Source code and pretrained models are available at
https://github.com/clovaai/CutMix-PyTorch .
