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• W2897636275 abstract "The improved generative adversarial network (improved GAN) is a successful method using a generative adversarial model to solve the problem of semi-supervised learning (SSL). The improved GAN learns a generator with the technique of mean feature matching which penalizes the discrepancy of the first-order moment of the latent features. To better describe common attributes of a distribution, this paper proposes a novel SSL method which incorporates the first-order and the secondorder moments of the features in an intermediate layer of the discriminator, called mean and variance feature matching GAN (MVFM-GAN). To capture more precisely the data manifold, not only the mean but also the variance is used in the latent feature learning. Compared with improved GAN and other traditional methods, MVFM-GAN achieves superior performance in semi-supervised classification tasks and a better stability of GAN training, particularly in the cases when the number of labeled samples is low. It shows a comparable performance with the state-of-the-art methods on several benchmark data sets. As a byproduct of the novel approach, MVFM-GAN generates realistic images of good visual quality." @default.
• W2897636275 created "2018-10-26" @default.
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• W2897636275 date "2019-12-01" @default.
• W2897636275 modified "2023-10-15" @default.
• W2897636275 title "Semi-Supervised Learning Based on GAN With Mean and Variance Feature Matching" @default.
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• W2897636275 doi "https://doi.org/10.1109/tcds.2018.2875462" @default.
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