Title: Recycling the discriminator for improving the inference mapping of GAN. Abstract: Generative adversarial networks (GANs) have achieved outstanding success in generating the high-quality data. Focusing on the generation process, existing GANs learn a unidirectional mapping from the latent vector to the data. Later, various studies point out that the latent space of GANs is semantically meaningful and can be utilized in advanced data analysis and manipulation. In order to analyze the real data in the latent space of GANs, it is necessary to investigate the inverse generation mapping from the data to the latent vector. To tackle this problem, the bidirectional generative models introduce an encoder to establish the inverse path of the generation process. Unfortunately, this effort leads to the degradation of generation quality because the imperfect generator rather interferes the encoder training and vice versa. 
In this paper, we propose an effective algorithm to infer the latent vector based on existing unidirectional GANs by preserving their generation quality.
It is important to note that we focus on increasing the accuracy and efficiency of the inference mapping but not influencing the GAN performance (i.e., the quality or the diversity of the generated sample).
Furthermore, utilizing the proposed inference mapping algorithm, we suggest a new metric for evaluating the GAN models by measuring the reconstruction error of unseen real data.
The experimental analysis demonstrates that the proposed algorithm achieves more accurate inference mapping than the existing method and provides the robust metric for evaluating GAN performance. 