@InProceedings{AriasRamí:2017:SeAp,
author = "Arias, Jhosimar George and Ram{\'{\i}}rez, Gerberth
Ad{\'{\i}}n",
title = "Learning to Cluster with Auxiliary Tasks: A Semi-Supervised
Approach",
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
classification.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
doi = "10.1109/SIBGRAPI.2017.25",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.25",
language = "en",
ibi = "8JMKD3MGPAW/3PFRBBL",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFRBBL",
targetfile = "138.pdf",
urlaccessdate = "2024, Apr. 29"
}