Machine Learning

Linking Generative Adversarial Learning and Binary Classification

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  • arXiv
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    Linking Generative Adversarial Learning and Binary Classification

    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.

    Linking Generative Adversarial Learning and Binary Classification
    by Akshay Balsubramani
    https://arxiv.org/pdf/1709.01509v1.pdf

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