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@InProceedings{PontiRibNazBuiCol:2017:EvYoWa,
               author = "Ponti, Moacir A. and Ribeiro, Leonardo S. F. and Nazar{\'e}, 
                         Tiago S. and Bui, Tu and Collomosse, John",
          affiliation = "{Universidade de S{\~a}o Paulo} and {Universidade de S{\~a}o 
                         Paulo} and {Universidade de S{\~a}o Paulo} and {University of 
                         Surrey} and {University of Surrey}",
                title = "Everything you wanted to know about Deep Learning for Computer 
                         Vision but were afraid to ask",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Computer Vision, Deep Learning, Image Processing, Video 
                         Processing.",
             abstract = "Deep Learning methods are currently the state-of-the-art in many 
                         Computer Vision and Image Processing problems, in particular image 
                         classification. After years of intensive investigation, a few 
                         models matured and became important tools, including Convolutional 
                         Neural Networks (CNNs), Siamese and Triplet Networks, 
                         Auto-Encoders (AEs) and Generative Adversarial Networks (GANs). 
                         The field is fast-paced and there is a lot of terminologies to 
                         catch up for those who want to adventure in Deep Learning waters. 
                         This paper has the objective to introduce the most fundamental 
                         concepts of Deep Learning for Computer Vision in particular CNNs, 
                         AEs and GANs, including architectures, inner workings and 
                         optimization. We offer an updated description of the theoretical 
                         and practical knowledge of working with those models. After that, 
                         we describe Siamese and Triplet Networks, not often covered in 
                         tutorial papers, as well as review the literature on recent and 
                         exciting topics such as visual stylization, pixel-wise prediction 
                         and video processing. Finally, we discuss the limitations of Deep 
                         Learning for Computer Vision.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PJAABB",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3PJAABB",
           targetfile = "
                         
                         _2017_sibgrapi__Tutorial_Deep_Learning_for_CV___Survey_Paper_CRP.pdf",
        urlaccessdate = "2021, Jan. 21"
}


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