  We present You Only Cut Once (YOCO) for performing data augmentations. YOCO
cuts one image into two pieces and performs data augmentations individually
within each piece. Applying YOCO improves the diversity of the augmentation per
sample and encourages neural networks to recognize objects from partial
information. YOCO enjoys the properties of parameter-free, easy usage, and
boosting almost all augmentations for free. Thorough experiments are conducted
to evaluate its effectiveness. We first demonstrate that YOCO can be seamlessly
applied to varying data augmentations, neural network architectures, and brings
performance gains on CIFAR and ImageNet classification tasks, sometimes
surpassing conventional image-level augmentation by large margins. Moreover, we
show YOCO benefits contrastive pre-training toward a more powerful
representation that can be better transferred to multiple downstream tasks.
Finally, we study a number of variants of YOCO and empirically analyze the
performance for respective settings. Code is available at GitHub.
