A Review of Semi Supervised Learning Theories and Recent Advances

Abstract

Semi-supervised learning, which has emerged from the beginning of this century, is a new type of learning method between traditional supervised learning and unsupervised learning. The main idea of semi-supervised learning is to introduce unlabeled samples into the model training process to avoid performance (or model) degeneration due to insufficiency of labeled samples. Semi-supervised learning has been applied successfully in many fields. This paper reviews the development process and main theories of semi-supervised learning, as well as its recent advances and importance in solving real-world problems demonstrated by typical application examples.

arXiv commentChinese language, 14 pages

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Cited by in corpus (3)

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• Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training
• A Diversity-Enhanced and Constraints-Relaxed Augmentation for Low-Resource Classification