@InProceedings{AlvarengaeSilvaAlme:2023:OpSeDo,
               author = "Alvarenga e Silva, Lucas Fernando and Almeida, Jurandy",
          affiliation = "{Universidade Estadual de Campinas – UNICAMP} and {Universidade 
                         Federal de S{\~a}o Carlos – UFScar}",
                title = "Open Set Domain Adaptation Methods in Deep Networks for Image 
                         Recognition",
            booktitle = "Proceedings...",
                 year = "2023",
               editor = "Clua, Esteban Walter Gonzalez and K{\"o}rting, Thales Sehn and 
                         Paulovich, Fernando Vieira and Feris, Rogerio",
         organization = "Conference on Graphics, Patterns and Images, 36. (SIBGRAPI)",
             keywords = "open set domain adaptation, unsupervised domain adaptation, domain 
                         adaptation, deep learning.",
             abstract = "Deep learning (DL) has revolutionized various fields through its 
                         remarkable capacity to learn from raw data. However, in 
                         uncontrolled environments like in the wild, the performance of 
                         these systems might degrade to some extent, especially with 
                         unlabeled datasets. Naive approaches train DL models on labeled 
                         datasets (source domains) that resemble the unlabeled test dataset 
                         (target domain), but nonetheless, this approach may not yield 
                         optimal results due to domain and category-shift problems. These 
                         issues have been the primary focus of Unsupervised Domain 
                         Adaptation (UDA) and Open Set Recognition research areas. To 
                         address the domain-shift problem, we introduced the Multi-Source 
                         Domain Alignment Layers (MS-DIAL), a structural solution for 
                         multi-source UDA. MS-DIAL aligns the source domains and the target 
                         domain at various levels of the feature space, individually 
                         achieving competitive results comparable to the state-of-the-art, 
                         and when combined with other UDA methods, it further enhances 
                         transferability by up to 30.64% in relative performance gains. 
                         Subsequently, we tackled the demanding setup of Open Set Domain 
                         Adaptation (OSDA), where both domain and category-shift issues 
                         coexist. Our proposed approach involves dealing with negatives, 
                         extracting a high-confidence set of unknown instances, and using 
                         them as a hard constraint to refine the classification boundaries 
                         of OSDA methods. We assessed our proposal in an extensive set of 
                         experiments, which achieved up to 5.8% of absolute performance 
                         gains.",
  conference-location = "Rio Grande, RS",
      conference-year = "Nov. 06-09, 2023",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/49S978P",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/49S978P",
           targetfile = "silva13.pdf",
        urlaccessdate = "2024, May 05"
}