  Machine learning has been utilized to perform tasks in many different domains
such as classification, object detection, image segmentation and natural
language analysis. Data labeling has always been one of the most important
tasks in machine learning. However, labeling large amounts of data increases
the monetary cost in machine learning. As a result, researchers started to
focus on reducing data annotation and labeling costs. Transfer learning was
designed and widely used as an efficient approach that can reasonably reduce
the negative impact of limited data, which in turn, reduces the data
preparation cost. Even transferring previous knowledge from a source domain
reduces the amount of data needed in a target domain. However, large amounts of
annotated data are still demanded to build robust models and improve the
prediction accuracy of the model. Therefore, researchers started to pay more
attention on auto annotation and labeling. In this survey paper, we provide a
review of previous techniques that focuses on optimized data annotation and
labeling for video, audio, and text data.
