While deep learning strategies achieve outstanding results in computer vision tasks, one issue remains: The current strategies rely heavily on a huge amount of labeled data. In many real-world problems, it is not feasible to create such an amount of labeled training data. Therefore, it is common to incorporate unlabeled data into the training process to reach equal results with fewer labels. Due to a lot of concurrent research, it is difficult to keep track of recent developments. In this survey, we provide an overview of often used ideas and methods in image classification with fewer labels. We compare 34 methods in detail based on their performance and their commonly used ideas rather than a fine-grained taxonomy. In our analysis, we identify three major trends that lead to future research opportunities. 1. State-of-the-art methods are scaleable to real-world applications in theory but issues like class imbalance, robustness, or fuzzy labels are not considered. 2. The degree of supervision which is needed to achieve comparable results to the usage of all labels is decreasing and therefore methods need to be extended to settings with a variable number of classes. 3. All methods share some common ideas but we identify clusters of methods that do not share many ideas. We show that combining ideas from different clusters can lead to better performance.
翻译:虽然深层次的学习战略在计算机愿景任务中取得了杰出的成果,但有一个问题仍然存在:目前的战略严重依赖大量贴标签的数据。在许多现实世界的问题中,创建如此大量贴标签的培训数据是不可行的。因此,将未贴标签的数据纳入培训过程以达到同等结果,使用较少标签是常见的。由于大量同时进行的研究,很难跟踪最近的发展动态。在本次调查中,我们提供了在图像分类方面通常使用较少标签的概念和方法的概览。我们比较了34种方法的细节,这些方法基于其性能和通常使用的想法,而不是精细的分类。在我们的分析中,我们发现了导致未来研究机会的三大趋势。1. 将非贴标签的数据方法在理论上可以推广到真实世界的应用,但是没有考虑诸如阶级不平衡、稳健或模糊标签等问题。 在本次调查中,为了取得与所有标签的使用相类似的结果而需要的监督程度正在下降,因此,方法需要扩大到不同类别的背景。3.所有方法都分享一些共同的想法,但我们可以分享不同的分组。