@InProceedings{NazareCostMellPont:2018:EmAnUs,
               author = "Nazare, Tiago S. and Costa, Gabriel B. Paranhos da and Mello, 
                         Rodrigo F. de and Ponti, Moacir A.",
          affiliation = "{University of S{\~a}o Paulo} and {University of S{\~a}o Paulo} 
                         and {University of S{\~a}o Paulo} and {University of S{\~a}o 
                         Paulo}",
                title = "Color quantization in transfer learning and noisy scenarios: an 
                         empirical analysis using convolutional networks",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Deep learning, transfer learning, convolutional neural networks, 
                         computer vision.",
             abstract = "Transfer learning is seen as one of the most promising areas of 
                         machine learning. Lately, features from pre-trained models have 
                         been used to achieve state-of-the-art results in several machine 
                         vision problems. Those models are usually employed when the 
                         problem of interest does not have enough supervised examples to 
                         support the network training from scratch. Most applications use 
                         networks pre-trained on noise-free RGB image datasets, what is 
                         observed even when the target domain counts on grayscale images or 
                         when data is degraded by noise. In this paper, we evaluate the use 
                         of Convolutional Neural Networks (CNNs) on such transfer learning 
                         scenarios and the impact of using RGB trained networks on 
                         grayscale image tasks. Our results confirm that the use of 
                         networks trained using colored images on grayscale tasks hinders 
                         the overall performance when compared to a similar network trained 
                         on a quantized version of the original dataset. Results also show 
                         that higher quantization levels (resulting in less colors) 
                         increase the robustness of CNN features in the presence of 
                         noise.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "29 Oct.-1 Nov. 2018",
                  doi = "10.1109/SIBGRAPI.2018.00055",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00055",
             language = "en",
                  ibi = "8JMKD3MGPAW/3RRA45S",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3RRA45S",
           targetfile = "SIB_2018.pdf",
        urlaccessdate = "2024, May 02"
}