@InProceedings{SilvaPedFarPapAlm:2021:ImTrDo,
               author = "Silva, Lucas Fernando Alvarenga e and Pedronette, Daniel Carlos 
                         Guimar{\~a}es and Faria, Fabio Augusto and Papa, Jo{\~a}o Paulo 
                         and Almeida, Jurandy",
          affiliation = "{Universidade Federal de S{\~a}o Paulo } and {S{\~a}o Paulo 
                         State University } and {Universidade Federal de S{\~a}o Paulo } 
                         and {S{\~a}o Paulo State University } and {Universidade Federal 
                         de S{\~a}o Paulo}",
                title = "Improving Transferability of Domain Adaptation Networks Through 
                         Domain Alignment Layers",
            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 = "deep learning, unsupervised domain adaptation, image 
                         recognition.",
             abstract = "Deep learning (DL) has been the primary approach used in various 
                         computer vision tasks due to its relevant results achieved on many 
                         tasks. However, on real-world scenarios with partially or no 
                         labeled data, DL methods are also prone to the well-known domain 
                         shift problem. Multi-source unsupervised domain adaptation (MSDA) 
                         aims at learning a predictor for an unlabeled domain by assigning 
                         weak knowledge from a bag of source models. However, most works 
                         conduct domain adaptation leveraging only the extracted features 
                         and reducing their domain shift from the perspective of loss 
                         function designs. In this paper, we argue that it is not 
                         sufficient to handle domain shift only based on domain-level 
                         features, but it is also essential to align such information on 
                         the feature space. Unlike previous works, we focus on the network 
                         design and propose to embed Multi-Source version of DomaIn 
                         Alignment Layers (MS-DIAL) at different levels of the predictor. 
                         These layers are designed to match the feature distributions 
                         between different domains and can be easily applied to various 
                         MSDA methods. To show the robustness of our approach, we conducted 
                         an extensive experimental evaluation considering two challenging 
                         scenarios: digit recognition and object classification. The 
                         experimental results indicated that our approach can improve 
                         state-of-the-art MSDA methods, yielding relative gains of up to 
                         +30.64% on their classification accuracies.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00031",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00031",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CPUQ2",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CPUQ2",
           targetfile = "sibgrapi95.pdf",
        urlaccessdate = "2024, May 06"
}