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Machine learning has been utilized to perform tasks in many different domainssuch as classification, object detection, image segmentation and naturallanguage analysis. Data labeling has always been one of the most importanttasks in machine learning. However, labeling large amounts of data increasesthe monetary cost in machine learning. As a result, researchers started tofocus on reducing data annotation and labeling costs. Transfer learning wasdesigned and widely used as an efficient approach that can reasonably reducethe negative impact of limited data, which in turn, reduces the datapreparation cost. Even transferring previous knowledge from a source domainreduces the amount of data needed in a target domain. However, large amounts ofannotated data are still demanded to build robust models and improve theprediction accuracy of the model. Therefore, researchers started to pay moreattention on auto annotation and labeling. In this survey paper, we provide areview of previous techniques that focuses on optimized data annotation andlabeling for video, audio, and text data.

A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text Data