@InProceedings{SantosThumPont:2021:DaAuGu,
               author = "Santos, Fernando Pereira dos and Thum{\'e}, Gabriela Salvador and 
                         Ponti, Moacir Antonelli",
          affiliation = "{Universidade de S{\~a}o Paulo } and {Universidade de S{\~a}o 
                         Paulo } and {Universidade de S{\~a}o Paulo}",
                title = "Data Augmentation Guidelines for Cross-Dataset Transfer Learning 
                         and Pseudo Labeling",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "transfer learning, deep learning, data augmentation.",
             abstract = "Convolutional Neural Networks require large amounts of labeled 
                         data in order to be trained. To improve such performances, a 
                         practical approach widely used is to augment the training set 
                         data, generating compatible data. Standard data augmentation for 
                         images includes conventional techniques, such as rotation, shift, 
                         and flip. In this paper, we go beyond such methods by studying 
                         alternative augmentation procedures for cross-dataset scenarios, 
                         in which a source dataset is used for training and a target 
                         dataset is used for testing. Through an extensive analysis 
                         considering different paradigms, saturation, and combination 
                         procedures, we provide guidelines for using augmentation methods 
                         in favor of transfer learning scenarios. As a novel approach for 
                         self-supervised learning, we also propose data augmentation 
                         techniques as pseudo labels during training. Our techniques 
                         demonstrate themselves as robust alternatives for different 
                         domains of transfer learning, including benefiting scenarios for 
                         self-supervised learning.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00036",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00036",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CFHKL",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CFHKL",
           targetfile = "paper112.pdf",
        urlaccessdate = "2024, May 06"
}