Generative adversarial networks (GANs) successfully generate high quality data by learning a mapping from a latent vector to the data. Various studies assert that the latent space of a GAN is semantically meaningful and can be utilized for advanced data analysis and manipulation. To analyze the real data in the latent space of a GAN, it is necessary to build an inference mapping from the data to the latent vector. This paper proposes an effective algorithm to accurately infer the latent vector by utilizing GAN discriminator features. Our primary goal is to increase inference mapping accuracy with minimal training overhead. Furthermore, using the proposed algorithm, we suggest a conditional image generation algorithm, namely a spatially conditioned GAN. Extensive evaluations confirmed that the proposed inference algorithm achieved more semantically accurate inference mapping than existing methods and can be successfully applied to advanced conditional image generation tasks.

生成对抗网络（GAN）通过学习从潜在向量到数据的映射，成功生成高质量数据。各种研究断言，GAN的潜在空间在语义上是有意义的，可以用于高级数据分析和处理。为了分析GAN潜在空间中的实际数据，有必要建立从数据到潜在向量的推理映射。本文提出了一种有效的算法，可以利用GAN鉴别特征准确地推导潜在矢量。我们的主要目标是以最少的培训开销来提高推理映射的准确性。此外，使用提出的算法，我们提出了一种条件图像生成算法，即空间条件GAN。