An Overview of Deep Semi-Supervised Learning
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Deep neural networks demonstrated their ability to provide remarkable performances on particular supervised learning tasks (e.g., image classification) when trained on extensive collections of labeled data (e.g., ImageNet). However, creating such large datasets requires a considerable amount of resources, time, and effort. Such resources may not be available in many practical cases, limiting the adoption and application of many deep learning methods. In a search for more data-efficient deep learning methods to overcome this need for large annotated datasets, we a rising research interest in recent years with regards to the application of semi-supervised learning to deep neural nets as a possible alternative, by developing novel methods and adopting existing semi-supervised learning frameworks for a deep learning setting. In this paper, we provide a comprehensive overview of deep semi-supervised learning, starting with an introduction to semi-supervised learning. Followed by a summarization of the dominant semi-supervised approaches in deep learning.
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