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1909.08542.pdf
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🔗 Source: arxiv.org
📊 Size: 3.43 MB
📄 Pages: 14 pages
⬇️ Downloads: 220

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Recent advances in generative models and adversarial training have led to a flourishing image-to-image (I2I) translation literature, with applications including semantic segmentation. Current I2I translation approaches require either paired or unpaired training data, with paired data often being costly and cumbersome to obtain. This paper proposes a method to bridge the gap between supervised and unsupervised I2I translation by selecting a small number of paired training samples and incorporating both paired and unpaired samples in the training process. The approach builds upon pix2pix and CycleGAN, state-of-the-art I2I translation techniques, and demonstrates significant improvements in both supervised and unsupervised I2I translation settings. Experiments show that using only one paired training sample can achieve performance comparable to supervised pix2pix and better than unsupervised CycleGAN.

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Recent advances in generative models and adversarial training have led to a flourishing image-to-image (I2I) translation literature, with applications...

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  • File Format: PDF
  • File Size: 3.43 MB
  • Pages: 14
  • Language: EN
  • Total Downloads: 220
  • Last Updated: 1 week ago

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