A Review of Semi Supervised Learning Theories and Recent Advances
It provides a comprehensive overview for researchers and practitioners, but is incremental as it synthesizes existing knowledge without introducing new methods.
The paper reviews the development, main theories, and recent advances in semi-supervised learning, highlighting its application in various fields to address performance issues from insufficient labeled data.
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.