12 Must Read Papers on Generative Adversarial Networks (GANs)

There are thousands of academic papers on Arxiv, so which ones should you read? I read hundreds of GANs papers while researching for my book and below are the 12 most influential papers (from 2014 to 2019) I found. There aren’t that many breakthrough GANs papers after 2019. Click the names and images to go to source.

  • Wasserstein GAN. This paper proves mathematically why GANs training is unstable. The Wasserstein loss is not widely used later but its approach of analysing GANs with mathematical rigour using Lipschitz constraint inspired innovations to make training GAN easier.
  • Conditional Generative Adversarial Nets. Earlier GANs generate images from random noise alone. This paper shows how to encode the class labels into embedding and use that to generate samples from desired class labels.
  • Image-to-Image Translation with Conditional Adversarial Networks. Pix2pix. The first image-to-image GAN that caught public attention including sketch-to-cat application. It also popularizes the use of PatchGAN (Precomputed real-time texture synthesis with markovian generative adversarial networks https://arxiv.org/abs/1604.04382) in discriminator to increase fidelity of generated images.
  • A Neural Algorithm of Artistic Style. Neural style transfer to convert photos into artistic painting. To me, this is the most underrated paper in image generation. This paper led the research in disentanglement where it separates the image into style and content. This eventually lead to creation of StyleGAN.

This list isn’t exhaustive but they are the important papers to prepare you to understand the state-of-the-art researchers. My prediction for 2021 (date of this article) is that where will be widespread use of transformer and fusing of language e.g. text-to-image like OpenAI’s DALL-E model.

Hope you enjoy reading this article. If you’re interest in implementing these models, then you can find these information in the book “Hands-on Image Generation with TensorFlow”. You can read the overview in https://soon-yau.medium.com/learn-and-master-ai-for-image-generation-423978e2f95f?sk=7ddc810a5f86021bc79792bf6af2eaed

https://www.amazon.com/dp/1838826785

Independent AI Consultant | Book author of “Hands-on Image Generation with TensorFlow” http://linkedin.com/in/soonyau

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