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A Survey on Adversarial Image Synthesis

arXiv 2106.16056 ↗First posted 30 Jun 2021cs.CVcited by 0

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Abstract (arXiv)

Generative Adversarial Networks (GANs) have been extremely successful in various application domains. Adversarial image synthesis has drawn increasing attention and made tremendous progress in recent years because of its wide range of applications in many computer vision and image processing problems. Among the many applications of GAN, image synthesis is the most well-studied one, and research in this area has already demonstrated the great potential of using GAN in image synthesis. In this paper, we provide a taxonomy of methods used in image synthesis, review different models for text-to-image synthesis and image-to-image translation, and discuss some evaluation metrics as well as possible future research directions in image synthesis with GAN.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 67 of its 83 references that are in the map. First posted on arXiv 30 Jun 2021.

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References · 67

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38 more
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