Abstract
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 this https URL.
Abstract (translated)
我们提出了一个名为You Only Need hAlf（YONA）的新数据增强方法，该方法简化了数据增强过程。YONA对图像进行分割，用噪声替换图像的一半，并应用数据增强技术对剩余的一半。这种方法减少了原始图像中的冗余信息，鼓励神经网络从 incomplete views 中识别物体，并显著增强了神经网络的鲁棒性。YONA的特点是参数免费、简单易用、增强各种现有的数据增强策略，从而提高了神经网络的鲁棒性，而无需增加额外的计算成本。为了证明YONA的有效性，进行了广泛的实验。这些实验证实了YONA与各种数据增强方法和神经网络架构的兼容性，在CIFAR分类任务中取得了显著的改进，有时甚至超过了传统的图像级数据增强方法。此外，YONA显著增强了神经网络对对抗攻击的鲁棒性。此外，通过探索YONA的变体，实验证明遮盖图像的一半可以优化性能。代码可在此处访问：https://www.thunar.mamail.com/
URL
https://arxiv.org/abs/2405.02830
