@InProceedings{FariaCarn:2020:WhArGe,
               author = "Faria, Fabio Augusto and Carneiro, Gustavo",
          affiliation = "{Universidade Federal de S{\~a}o Paulo} and {The University of 
                         Adelaide}",
                title = "Why are Generative Adversarial Networks so Fascinating and 
                         Annoying?",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and 
                         Pelechano, Nuria and Wang, Zhangyang (Atlas)",
         organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "GAN, machine learning, computer vision, deep learning.",
             abstract = "This paper focuses on one of the most fascinating and successful, 
                         but challenging generative models in the literature: the 
                         Generative Adversarial Networks (GAN). Recently, GAN has attracted 
                         much attention by the scientific community and the entertainment 
                         industry due to its effectiveness in generating complex and 
                         high-dimension data, which makes it a superior model for producing 
                         new samples, compared with other types of generative models. The 
                         traditional GAN (referred to as the Vanilla GAN) is composed of 
                         two neural networks, a generator and a discriminator, which are 
                         modeled using a minimax optimization. The generator creates 
                         samples to fool the discriminator that in turn tries to 
                         distinguish between the original and created samples. This 
                         optimization aims to train a model that can generate samples from 
                         the training set distribution. In addition to defining and 
                         explaining the Vanilla GAN and its main variations (e.g., DCGAN, 
                         WGAN, and SAGAN), this paper will present several applications 
                         that make GAN an extremely exciting method for the entertainment 
                         industry (e.g., style-transfer and image-to-image translation). 
                         Finally, the following measures to assess the quality of generated 
                         images are presented: Inception Search (IS), and Frechet Inception 
                         Distance (FID).",
  conference-location = "Porto de Galinhas (virtual)",
      conference-year = "7-10 Nov. 2020",
                  doi = "10.1109/SIBGRAPI51738.2020.00009",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI51738.2020.00009",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/43BFBQE",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/43BFBQE",
           targetfile = "GAN_Tutorial_SIBGRAPI2020.pdf",
        urlaccessdate = "2024, Apr. 27"
}