A Survey on Deep Semi-Supervised Learning

Deep semi-supervised learning is a fast-growing field with a range of practical applications. Then we provide a comprehensive review of 60 representative methods and offer a detailed comparison of these methods in terms of the type of losses, architecture differences, and test performance results.

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• 1 This paper provides a comprehensive survey on both fundamentals and recent advances in deep semi-supervised learning methods from perspectives of model design and unsupervised loss functions.
• 2 We first present a taxonomy for deep semi-supervised learning that categorizes existing methods, including deep generative methods, consistency regularization methods, graph-based methods, pseudo-labeling methods, and hybrid methods.
• 3 Then we provide a comprehensive review of 60 representative methods and offer a detailed comparison of these methods in terms of the type of losses, architecture differences, and test performance results.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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