Neural networks are prone to overfitting and memorizing data patterns. To avoid over-fitting and enhance their generalization and performance, various methods have been suggested in the literature, including dropout, regularization, label smoothing, etc. One such method is augmentation which introduces different types of corruption in the data to prevent the model from overfitting and to memorize patterns present in the data. A sub-area of data augmentation is image mixing and deleting. This specific type of augmentation either deletes image regions or mixes two images to hide or make particular characteristics of images confusing for the network, forcing it to emphasize the overall structure of the object in an image. Models trained with this approach have proven to perform and generalize well compared to those trained without image mixing or deleting. An added benefit that comes with this method of training is robustness against image corruption. Due to its low computational cost and recent success, researchers have proposed many image mixing and deleting techniques. We furnish an in-depth survey of image mixing and deleting techniques and provide categorization via their most distinguishing features. We initiate our discussion with some fundamental relevant concepts. Next, we present essentials, such as each category's strengths and limitations, describing their working mechanism, basic formulations, and applications. We also discuss the general challenges and recommend possible future research directions for image mixing and deleting data augmentation techniques. Datasets and codes for evaluation are publicly available here.

翻译：神经网络容易过拟合和记忆数据模式。为避免过拟合并增强其泛化能力和性能，文献中提出了多种方法，包括dropout、正则化、标签平滑等。其中一种方法是数据增强，它通过在数据中引入不同类型的噪声来防止模型过拟合和记忆数据中的模式。数据增强的一个子领域是图像混合与删除。这种特定类型的增强要么删除图像区域，要么混合两幅图像，以隐藏或混淆网络对图像特定特征的关注，迫使网络关注图像中物体的整体结构。采用这种方法训练的模型已被证明，与未使用图像混合或删除训练的模型相比，其性能和泛化能力更优。此类训练方法的一个额外优势是对图像噪声的鲁棒性。由于其计算成本低且近期取得了成功，研究人员提出了许多图像混合与删除技术。我们对图像混合与删除技术进行了深入的综述，并按其最显著的特征进行分类。我们首先讨论一些基本的相关概念。接着，我们呈现每类技术的优缺点等要点，描述其工作机制、基本公式和应用。我们还讨论了常见的挑战，并为图像混合与删除数据增强技术推荐了可能的未来研究方向。用于评估的数据集和代码已在此处公开。