@InProceedings{LopesJrSchw:2021:AnEfDi,
               author = "Lopes Junior, Renato Sergio and Schwartz, William Robson",
          affiliation = "{Universidade Federal de Minas Gerais } and {Universidade Federal 
                         de Minas Gerais}",
                title = "Analyzing the Effects of Dimensionality Reduction for Unsupervised 
                         Domain Adaptation",
            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 = "computer vision, machine learning, domain adaptation, transfer 
                         learning.",
             abstract = "Deep neural networks are extensively used for solving a variety of 
                         computer vision problems. However, in order for these networks to 
                         obtain good results, a large amount of data is necessary for 
                         training. In image classification, this training data consists of 
                         images and labels that indicate the class portrayed by each image. 
                         Obtaining this large labeled dataset is very time and resource 
                         consuming. Therefore, domain adaptation methods allow different, 
                         but semantic-related, datasets that are already labeled to be used 
                         during training, thus eliminating the labeling cost. In this work, 
                         the effects of embedding dimensionality reduction in a 
                         state-of-the-art domain adaptation method are analyzed. 
                         Furthermore, we experiment with a different approach that use the 
                         available data from all domains to compute the confidence of 
                         pseudo-labeled samples. We show through experiments in commonly 
                         used datasets that, in fact, the proposed modifications led to 
                         better results in the target domain in some scenarios.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00019",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00019",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CQ8ML",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CQ8ML",
           targetfile = "78_final.pdf",
        urlaccessdate = "2024, May 06"
}