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      Cartoon-to-real: An Approach to Translate Cartoon to Realistic Images using GAN

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          Abstract

          We propose a method to translate cartoon images to real world images using Generative Aderserial Network (GAN). Existing GAN-based image-to-image translation methods which are trained on paired datasets are impractical as the data is difficult to accumulate. Therefore, in this paper we exploit the Cycle-Consistent Adversarial Networks (CycleGAN) method for images translation which needs an unpaired dataset. By applying CycleGAN we show that our model is able to generate meaningful real world images from cartoon images. However, we implement another state of the art technique \(-\) Deep Analogy \(-\) to compare the performance of our approach.

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          Author and article information

          Journal
          28 November 2018
          Article
          1811.11796
          f14f29a9-5444-4d67-8693-2cde2b926e2a

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          History
          Custom metadata
          2 pages, Accepted (as poster) in International Conference on Innovation in Engineering and Technology (ICIET) 2018, 27 - 28 Dec 2018, Dhaka, Bangladesh
          cs.CV

          Computer vision & Pattern recognition
          Computer vision & Pattern recognition

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