In recent years, one of the most popular techniques in the computer vision community has been the deep learning technique. As a data-driven technique, deep model requires enormous amounts of accurately labelled training data, which is often inaccessible in many real-world applications. A data-space solution is Data Augmentation (DA), that can artificially generate new images out of original samples. Image augmentation strategies can vary by dataset, as different data types might require different augmentations to facilitate model training. However, the design of DA policies has been largely decided by the human experts with domain knowledge, which is considered to be highly subjective and error-prone. To mitigate such problem, a novel direction is to automatically learn the image augmentation policies from the given dataset using Automated Data Augmentation (AutoDA) techniques. The goal of AutoDA models is to find the optimal DA policies that can maximize the model performance gains. This survey discusses the underlying reasons of the emergence of AutoDA technology from the perspective of image classification. We identify three key components of a standard AutoDA model: a search space, a search algorithm and an evaluation function. Based on their architecture, we provide a systematic taxonomy of existing image AutoDA approaches. This paper presents the major works in AutoDA field, discussing their pros and cons, and proposing several potential directions for future improvements.
翻译:近年来,计算机视觉界最受欢迎的技术之一是深层次的学习技术。作为数据驱动技术,深层次模型需要大量贴有准确标签的培训数据,而许多现实应用中往往无法获得这些数据。数据-空间解决方案是数据增强(DA),它可以人工从原始样本中生成新的图像。图像增强战略可以因数据集而异,因为不同的数据类型可能需要不同的增强,以便利模型培训。然而,设计数据扩展战略在很大程度上是由拥有域知识的人类专家决定的,认为域知识是高度主观的和容易出错的。为了缓解这一问题,一个新的方向是利用自动化数据增强(AutoDA)技术自动从给定数据集中自动学习图像增强政策。数据增强模型的目标是找到最佳的DA政策,使模型的性能收益最大化。这项调查从图像分类的角度讨论了自动数据开发系统技术的出现的根本原因。我们确定了标准的AutoDA模型的三个关键组成部分:搜索空间、搜索算法和评价职能。基于他们的架构,我们提供了一种系统化的增强图像增强政策。我们提供了一种系统化的系统化的分类方法,并提出了现有的图像改进方法。