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[00856] Successive image generation though cyclic transformations using CycleGAN

  • Session Time & Room : 3C (Aug.23, 13:20-15:00) @E811
  • Type : Contributed Talk
  • Abstract : CycleGAN is a deep generative adversarial networks that performs image to image style translation by learning relationship between two image domains. By using CycleGAN, here we developed a model performing cyclical transformation that generates a series of similar images. This system can be regarded as a dynamical system; it can continuously sample various images along the trajectory of the dynamical system. The chaotic behavior of this deep model was studied.
  • Classification : 68T07, 37N99
  • Format : Talk at Waseda University
  • Author(s) :
    • Takaya Tanaka (Graduate School of Engineering, Fukuoka Institute of Technology)
    • Takaya Tanaka (Graduate School of Engineering, Fukuoka Institute of Technology)
    • Yutaka Yamaguti (Faculty of Information Engineering, Fukuoka Institute of Technology)