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System and method for constructing a generative adversarial network model for image classification based on semi-supervised learning
System and method for constructing a generative adversarial network model for image classification based on semi-supervised learning
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机译：基于半监督学习构建用于图像分类的生成对抗网络模型的系统和方法
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摘要
The present invention relates to a method of constructing a hostile generation model based on quasi-supervised learning. The hostile generation model construction method includes the steps of: (a) obtaining a first loss function for a discriminator according to quasi-supervised learning, and learning the first loss function to be optimized; (b) obtaining a second loss function for minimizing the Earth Mover's distance (EM distance) with respect to the discriminator, and learning the second loss function to be optimized; (c) obtaining a third loss function according to an existing hostile generation model with respect to the generator, and learning the third loss function to be optimized; (d) obtaining a fourth loss function for minimizing the EM distance with respect to the generator, and learning the fourth loss function to be optimized; (e) classifying the result of the discriminator using a classifier and providing an output value.
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