  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.
