@InProceedings{SouzaMarqGois:2022:FuChGe,
               author = "Souza, Vinicius Luis Trevisan de and Marques, Bruno Augusto Dorta 
                         and Gois, Jo{\~a}o Paulo",
          affiliation = "{Universidade Federal do ABC} and {Universidade Federal do ABC} 
                         and {Universidade Federal do ABC}",
                title = "Fundamentals and Challenges of Generative Adversarial Networks for 
                         Image-based Applications",
            booktitle = "Proceedings...",
                 year = "2022",
         organization = "Conference on Graphics, Patterns and Images, 35. (SIBGRAPI)",
             keywords = "Generative Adversarial Network, image manipulation, deep image 
                         synthesis, deep neural network.",
             abstract = "Significant advances in image-based applications have been 
                         achieved in recent years, many of which are arguably due to recent 
                         developments in Generative Adversarial Networks (GANs). Although 
                         the continuous improvement in the architectures of GAN has 
                         significantly increased the quality of synthetic images, this is 
                         not without challenges such as training stability and convergence 
                         issues, to name a few. In this work, we present the fundamentals 
                         and notable architectures of GANs, especially for image-based 
                         applications. We also discuss relevant issues such as training 
                         problems, diversity generation, and quality assessment 
                         (metrics).",
  conference-location = "Natal, RN",
      conference-year = "24-27 Oct. 2022",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/47MNG5P",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/47MNG5P",
           targetfile = "trevisandesouza-1.pdf",
        urlaccessdate = "2024, Apr. 27"
}