# An Introduction to Image Synthesis with Generative Adversarial Nets

arXiv: https://arxiv.org/abs/1803.04469 · HTML: https://mlsanity.com/paper/1803.04469/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2018.

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- [Text to Image Synthesis using Stacked Conditional Variational Autoencoders and Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2207.03332/) (2022)
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## Abstract (arXiv)

There has been a drastic growth of research in Generative Adversarial Nets (GANs) in the past few years. Proposed in 2014, GAN has been applied to various applications such as computer vision and natural language processing, and achieves impressive performance. 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.