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