Discriminator Feature-Based Inference by Recycling the Discriminator of GANs

Duhyeon Bang, Seoungyoon Kang, Hyunjung Shim

Research output: Contribution to journalArticle


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.

Original languageEnglish
JournalInternational Journal of Computer Vision
Publication statusAccepted/In press - 2020

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Discriminator Feature-Based Inference by Recycling the Discriminator of GANs'. Together they form a unique fingerprint.

  • Cite this