  We propose a novel data augmentation method termed You Only Need hAlf (YONA),
which simplifies the augmentation process. YONA bisects an image, substitutes
one half with noise, and applies data augmentation techniques to the remaining
half. This method reduces the redundant information in the original image,
encourages neural networks to recognize objects from incomplete views, and
significantly enhances neural networks' robustness. YONA is distinguished by
its properties of parameter-free, straightforward application, enhancing
various existing data augmentation strategies, and thereby bolstering neural
networks' robustness without additional computational cost. To demonstrate
YONA's efficacy, extensive experiments were carried out. These experiments
confirm YONA's compatibility with diverse data augmentation methods and neural
network architectures, yielding substantial improvements in CIFAR
classification tasks, sometimes outperforming conventional image-level data
augmentation methods. Furthermore, YONA markedly increases the resilience of
neural networks to adversarial attacks. Additional experiments exploring YONA's
variants conclusively show that masking half of an image optimizes performance.
The code is available at https://github.com/HansMoe/YONA.
