TRAINING A CLASS-CONDITIONAL GENERATIVE ADVERSARIAL NETWORK

摘要

A computer-implemented method and system are described for training a class-conditional generative adversarial network (GAN). The discriminator is trained using a classification loss function while omitting using an adversarial loss function. Instead, if the training data has C classes, the classification loss function is formulated as a 2C-class classification problem, by which the discriminator is trained to distinguish 2 times C classes. Such trained discriminator provides an informative training signal for the generator to learn the class-conditional data synthesis by the generator. A data synthesis system and computer-implemented method are also described for synthesizing data using the generative part of the trained generative adversarial network.