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SYSTEM AND METHOD FOR SEMI-SUPERVISED CONDITIONAL GENERATIVE MODELING USING ADVERSARIAL NETWORKS
SYSTEM AND METHOD FOR SEMI-SUPERVISED CONDITIONAL GENERATIVE MODELING USING ADVERSARIAL NETWORKS
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机译:利用对抗网络进行半监督条件发电建模的系统和方法
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摘要
One embodiment facilitates generating synthetic data objects using a semi-supervised GAN. During operation, a generator module synthesizes a data object derived from a noise vector and an attribute label. The system passes, to an unsupervised discriminator module, the data object and a set of training objects which are obtained from a training data set. The unsupervised discriminator module calculates: a value indicating a probability that the data object is real; and a latent feature representation of the data object. The system passes the latent feature representation and the attribute label to a supervised discriminator module. The supervised discriminator module calculates a value indicating a probability that the attribute label given the data object is real. The system performs the aforementioned steps iteratively until the generator module produces data objects with a given attribute label which the unsupervised and supervised discriminator modules can no longer identify as fake.
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