# Linking Generative Adversarial Learning and Binary Classification

@article{Balsubramani2017LinkingGA, title={Linking Generative Adversarial Learning and Binary Classification}, author={A. Balsubramani}, journal={ArXiv}, year={2017}, volume={abs/1709.01509} }

In this note, we point out a basic link between generative adversarial (GA) training and binary classification -- any powerful discriminator essentially computes an (f-)divergence between real and generated samples. The result, repeatedly re-derived in decision theory, has implications for GA Networks (GANs), providing an alternative perspective on training f-GANs by designing the discriminator loss function.

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