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SELF-TRAINING METHOD AND SYSTEM FOR SEMI-SUPERVISED LEARNING WITH GENERATIVE ADVERSARIAL NETWORKS
SELF-TRAINING METHOD AND SYSTEM FOR SEMI-SUPERVISED LEARNING WITH GENERATIVE ADVERSARIAL NETWORKS
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机译:基于生成式对抗网络的半监督学习的自学方法和系统
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摘要
A method and system for augmenting a training dataset for a generative adversarial network (GAN). The training dataset includes labelled data samples and unlabelled data samples. The method includes: receiving generated samples generated using a first neural network of the GAN and the unlabelled samples of training dataset; determining a decision value for a sample from a decision function, wherein the sample is a generated sample of the generated samples or an unlabelled sample of the unlabelled samples of the training dataset; comparing the decision value to a threshold; in response to determining that the decision value exceeds the threshold: predicting a label for a sample; assigning the label to the sample; and augmenting the training dataset to include the sample with the assigned label as a labelled sample.
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