Generative AI Research Papers 📚

Generative AI has revolutionized the field of machine learning, enabling models to create new content such as images, text, and audio. Here are some landmark research papers that shaped this domain:

• Generative Adversarial Networks (GANs)
  Keyword: GANs
  Introduce by Ian Goodfellow et al. in 2014, GANs use adversarial training to generate realistic data.

  

• Transformer Models
  Keyword: Transformer_Models
  The 2017 paper by Vaswani et al. introduced the Transformer architecture, pivotal for natural language processing and image generation.

  

• Diffusion Models
  Keyword: Diffusion_Models
  Research from 2020 by Ho et al. demonstrated diffusion models' effectiveness in generating high-quality images.

  

• Variational Autoencoders (VAEs)
  Keyword: Variational_Autoencoders
  A foundational work from 2013 by Kingma and Welling, VAEs combine deep learning with probabilistic modeling for content generation.

  

For deeper exploration, check our Generative AI Overview page. 🌐