Improving Sample-based Evaluation for Generative Adversarial Networks

ICLR 2019 Shaohui Liu*Yi Wei*Jiwen LuJie Zhou

In this paper, we propose an improved quantitative evaluation framework for Generative Adversarial Networks (GANs) on generating domain-specific images, where we improve conventional evaluation methods on two levels: the feature representation and the evaluation metric. Unlike most existing evaluation frameworks which transfer the representation of ImageNet inception model to map images onto the feature space, our framework uses a specialized encoder to acquire fine-grained domain-specific representation... (read more)

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