@InProceedings{JordãoSchw:2021:DeSpOd,
               author = "Jord{\~a}o, Artur and Schwartz, William Robson",
          affiliation = "{Federal University of Minas Gerais} and {Federal University of 
                         Minas Gerais}",
                title = "Partial Least Squares: A Deep Space Odyssey",
            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 = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Computing, computer vision, estimation theory, pattern 
                         recognition.",
             abstract = "Modern visual pattern recognition models are based on deep 
                         convolutional networks. Such models are computationally expensive, 
                         hindering applicability on resource-constrained devices. To handle 
                         this problem, we propose three strategies. The first removes 
                         unimportant structures (neurons or layers) of convolutional 
                         networks, reducing their computational cost. The second inserts 
                         structures to design architectures automatically, enabling us to 
                         build high-performance networks. The third combines multiple 
                         layers of convolutional networks, enhancing data representation at 
                         negligible additional cost. These strategies are based on Partial 
                         Least Squares (PLS) which, despite promising results, is 
                         infeasible on large datasets due to memory constraints. To address 
                         this issue, we also propose a discriminative and low-complexity 
                         incremental PLS that learns a compact representation of the data 
                         using a single sample at a time, thus enabling applicability on 
                         large datasets. We assess the effectiveness of our approaches on 
                         several convolutional architectures and computer vision tasks, 
                         which include image classification, face verification and activity 
                         recognition. Our approaches reduce the resource overhead of both 
                         convolutional networks and Partial Least Squares, promoting 
                         energy- and hardware-friendly models for the academy and industry 
                         scenarios. Compared to state-of-the-art methods for the same 
                         purpose, we obtain one of the best trade-offs between predictive 
                         ability and computational cost.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CTEAE",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CTEAE",
           targetfile = "Article.pdf",
        urlaccessdate = "2024, May 06"
}