@InProceedings{MoraesEvanFernMart:2021:GeCoOu,
               author = "Moraes, Rog{\'e}rio Ferreira de and Evangelista, Raphael dos S. 
                         and Fernandes, Leandro A. F. and Mart{\'{\i}}, Luis",
          affiliation = "Universidade Federal Fluminense (UFF), Niter{\'o}i, Brazil  and 
                         Universidade Federal Fluminense (UFF), Niter{\'o}i, Brazil  and 
                         Universidade Federal Fluminense (UFF), Niter{\'o}i, Brazil  and 
                         Inria Chile Research Center, Las Condes, Chile",
                title = "GCOOD: A Generic Coupled Out-of-Distribution Detector for Robust 
                         Classification",
            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 = "OOD, Voronoi diagrams.",
             abstract = "Neural networks have achieved high degrees of accuracy in 
                         classification tasks. However, when an out-of-distribution (OOD) 
                         sample (\emph{i.e.,}~entries from unknown classes) is submitted 
                         to the classification process, the result is the association of 
                         the sample to one or more of the trained classes with different 
                         degrees of confidence. If any of these confidence values are more 
                         significant than the user-defined threshold, the network will 
                         mislabel the sample, affecting the model credibility. The 
                         definition of the acceptance threshold itself is a sensitive issue 
                         in the face of the classifier's overconfidence. This paper 
                         presents the Generic Coupled OOD Detector (GCOOD), a novel 
                         Convolutional Neural Network (CNN) tailored to detect whether an 
                         entry submitted to a trained classification model is an OOD sample 
                         for that model. From the analysis of the Softmax output of any 
                         classifier, our approach can indicate whether the resulting 
                         classification should be considered or not as a sample of some of 
                         the trained classes. To train our CNN, we had to develop a novel 
                         training strategy based on Voronoi diagrams of the location of 
                         representative entries in the latent space of the classification 
                         model and graph coloring. We evaluated our approach using ResNet, 
                         VGG, DenseNet, and SqueezeNet classifiers with images from the 
                         CIFAR-10 dataset.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00062",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00062",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45EACT2",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45EACT2",
           targetfile = "2021___Moraes_et_al____SIBGRAPI.pdf",
        urlaccessdate = "2024, May 06"
}