Data augmentation has been widely used to improve deep nerual networks performance. Numerous approaches are suggested, for example, dropout, regularization and image augmentation, to avoid over-ftting and enhancing generalization of neural networks. One of the sub-area within data augmentation is image mixing and deleting. This specific type of augmentation either mixes two images or delete image regions to hide or make certain characteristics of images confusing for the network to force it to emphasize on overall structure of object in image. The model trained with this approach has shown to perform and generalize well as compared to one trained without imgage mixing or deleting. Additional benefit achieved with this method of training is robustness against image corruptions. Due to its low compute cost and success in recent past, many techniques of image mixing and deleting are proposed. This paper provides detailed review on these devised approaches, dividing augmentation strategies in three main categories cut and delete, cut and mix and mixup. The second part of paper emprically evaluates these approaches for image classification, finegrained image recognition and object detection where it is shown that this category of data augmentation improves the overall performance for deep neural networks.

翻译：数据增强已广泛应用于提升深度神经网络性能。为避免过拟合并增强神经网络的泛化能力，学者提出了多种方法，例如Dropout、正则化和图像增强。数据增强中的子领域之一是图像混合与删除。这类特定增强方法通过混合两张图像或删除图像区域来隐藏或混淆图像的某些特征，迫使网络关注图像中目标的整体结构。实验表明，采用该方法训练的模型在性能和泛化能力上均优于未使用图像混合或删除训练的模型。此外，该类训练方法还带来了对图像损坏的鲁棒性优势。由于计算成本低且近年来表现优异，图像混合与删除技术被大量提出。本文对这些方法进行了详细综述，将增强策略划分为三个主要类别：裁剪删除、裁剪混合和Mixup。论文第二部分通过图像分类、细粒度图像识别和目标检测实验对这些方法进行实证评估，结果表明该类数据增强能够显著提升深度神经网络的整体性能。