@InProceedings{PeixinhoBenaNonaFalc:2018:DeTrDa,
               author = "Peixinho, Alan Zanoni and Benato, B{\'a}rbara Caroline and 
                         Nonato, Luis Gustavo and Falc{\~a}o, Alexandre Xavier",
          affiliation = "{University of Campinas} and {University of Campinas} and 
                         {University of S{\~a}o Paulo} and {University of Campinas}",
                title = "Delaunay Triangulation Data Augmentation guided by Visual 
                         Analytics for Deep Learning",
            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 = "Delaunay Triangulation, Data Augmentation, Visual Analytics, Deep 
                         Learning, Encoder-Decoder Neural Network, Convolutional Neural 
                         Network.",
             abstract = "It is well known that image classification problems can be 
                         effectively solved by Convolutional Neural Networks (CNNs). 
                         However, the number of supervised training examples from all 
                         categories must be high enough to avoid model over- fitting. In 
                         this case, two key alternatives are usually presented (a) the 
                         generation of artificial examples, known as data aug- mentation, 
                         and (b) reusing a CNN previously trained over a large supervised 
                         training set from another image classification problem a strategy 
                         known as transfer learning. Deep learning approaches have rarely 
                         exploited the superior ability of humans for cognitive tasks 
                         during the machine learning loop. We advocate that the expert 
                         intervention through visual analytics can improve machine 
                         learning. In this work, we demonstrate this claim by proposing a 
                         data augmentation framework based on Encoder- Decoder Neural 
                         Networks (EDNNs) and visual analytics for the design of more 
                         effective CNN-based image classifiers. An EDNN is initially 
                         trained such that its encoder extracts a feature vector from each 
                         training image. These samples are projected from the encoder 
                         feature space on to a 2D coordinate space. The expert includes 
                         points to the projection space and the feature vectors of the new 
                         samples are obtained on the original feature space by 
                         interpolation. The decoder generates artificial images from the 
                         feature vectors of the new samples and the augmented training set 
                         is used to improve the CNN-based classifier. We evaluate methods 
                         for the proposed framework and demonstrate its advantages using 
                         data from a real problem as case study the diagnosis of helminth 
                         eggs in humans. We also show that transfer learning and data 
                         augmentation by affine transformations can further improve the 
                         results.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "29 Oct.-1 Nov. 2018",
                  doi = "10.1109/SIBGRAPI.2018.00056",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00056",
             language = "en",
                  ibi = "8JMKD3MGPAW/3RNND3S",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3RNND3S",
           targetfile = "PID5546301.pdf",
        urlaccessdate = "2024, May 19"
}