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Automaitc Generation of Fashion Image Dataset by Using Progressive Growing GAN

PG-GAN을 이용한 패션이미지 데이터 자동 생성

  • Kim, Yanghee (Department of Computer Science Engineering, College of Informatics, Korea University) ;
  • Lee, Chanhee (Department of Computer Science Engineering, College of Informatics, Korea University) ;
  • Whang, Taesun (Department of Computer Science Engineering, College of Informatics, Korea University) ;
  • Kim, Gyeongmin (Department of Computer Science Engineering, College of Informatics, Korea University) ;
  • Lim, Heuiseok (Department of Computer Science Engineering, College of Informatics, Korea University)
  • 김양희 (고려대학교 정보대학 컴퓨터학과) ;
  • 이찬희 (고려대학교 정보대학 컴퓨터학과) ;
  • 황태선 (고려대학교 정보대학 컴퓨터학과) ;
  • 김경민 (고려대학교 정보대학 컴퓨터학과) ;
  • 임희석 (고려대학교 정보대학 컴퓨터학과)
  • Received : 2018.06.24
  • Accepted : 2018.09.16
  • Published : 2018.12.31

Abstract

Techniques for generating new sample data from higher dimensional data such as images have been utilized variously for speech synthesis, image conversion and image restoration. This paper adopts Progressive Growing of Generative Adversarial Networks(PG-GANs) as an implementation model to generate high-resolution images and to enhance variation of the generated images, and applied it to fashion image data. PG-GANs allows the generator and discriminator to progressively learn at the same time, continuously adding new layers from low-resolution images to result high-resolution images. We also proposed a Mini-batch Discrimination method to increase the diversity of generated data, and proposed a Sliced Wasserstein Distance(SWD) evaluation method instead of the existing MS-SSIM to evaluate the GAN model.

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