Discriminator Feature-Based Inference by Recycling the Discriminator of GANs

Duhyeon Bang, Seoungyoon Kang, Hyunjung Shim

Research output: Contribution to journalArticlepeer-review

Abstract

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
Pages (from-to)2436-2458
Number of pages23
JournalInternational Journal of Computer Vision
Volume128
Issue number10-11
DOIs
Publication statusPublished - 2020 Nov 1

Bibliographical note

Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Korean Government (Grant NRF-2019R1A2C2006123), the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2016-0-00288) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation), and also by ICT R&D program of MSIP/IITP. [R7124-16-0004, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding].

Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Korean Government (Grant NRF-2019R1A2C2006123), the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2016-0-00288) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation), and also by ICT R&D program of MSIP/IITP. [R7124-16-0004, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding].

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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