The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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U-net and Residual-based Cycle-GAN for Improving Object Transfiguration Performance

물체 변형 성능을 향상하기 위한 U-net 및 Residual 기반의 Cycle-GAN

  • Received : 2018.01.16
  • Accepted : 2018.02.20
  • Published : 2018.02.28
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Abstract

The image-to-image translation is one of the deep learning applications using image data. In this paper, we aim at improving the performance of object transfiguration which transforms a specific object in an image into another specific object. For object transfiguration, it is required to transform only the target object and maintain background images. In the existing results, however, it is observed that other parts in the image are also transformed. In this paper, we have focused on the structure of artificial neural networks that are frequently used in the existing methods and have improved the performance by adding constraints to the exiting structure. We also propose the advanced structure that combines the existing structures to maintain their advantages and complement their drawbacks. The effectiveness of the proposed methods are shown in experimental results.

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References

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