Optimization of semi-supervised generative adversarial network models: a survey
Yongqing Ma,
Yifeng Zheng,
Wenjie Zhang
et al.
Abstract:PurposeWith the development of intelligent technology, deep learning has made significant progress and has been widely used in various fields. Deep learning is data-driven, and its training process requires a large amount of data to improve model performance. However, labeled data is expensive and not readily available.Design/methodology/approachTo address the above problem, researchers have integrated semi-supervised and deep learning, using a limited number of labeled data and many unlabeled data to train mo… Show more
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