author = "Arias, Jhosimar George and Ram{\'{\i}}rez, Gerberth 
                title = "Learning to Cluster with Auxiliary Tasks: A Semi-Supervised 
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "deep learning, generative models, clustering, semi-supervised 
                         learning, probabilistic models.",
             abstract = "In this paper, we propose a model to learn a feature-category 
                         latent representation of the data, that is guided by a 
                         semi-supervised auxiliary task. The goal of this auxiliary task is 
                         to assign labels to unlabeled data and regularize the feature 
                         space. Our model is represented by a modified version of a 
                         Categorical Variational Autoencoder, i.e., a probabilistic 
                         generative model that approximates a categorical distribution with 
                         variational inference. We benefit from the autoencoders 
                         architecture to learn powerful representations with Deep Neural 
                         Networks in an unsupervised way, and to optimize the model with 
                         semi-supervised tasks. We derived a loss function that integrates 
                         the probabilistic model with our auxiliary task to guide the 
                         learning process. Experimental results show the effectiveness of 
                         our method achieving more than 90% of clustering accuracy by using 
                         only 100 labeled examples. Moreover we show that the learned 
                         features have discriminative properties that can be used for 
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
           targetfile = "138.pdf",
        urlaccessdate = "2021, Jan. 21"