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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Semi-supervised learning with connectivity-driven convolutional neural networks

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Amorim, Willian Paraguassu [1] ; Rosa, Gustavo Henrique [2] ; Thomazella, Rogerio [2] ; Cogo Castanho, Jose Eduardo [2] ; Lofrano Dotto, Fabio Romano [2] ; Rodrigues Junior, Oswaldo Pons [3] ; Marana, Aparecido Nilceu [2] ; Papa, Joao Paulo [2]
Total Authors: 8
[1] Fed Univ Grande Dourados, BR-79804970 Dourados, MS - Brazil
[2] Sao Paulo State Univ UNESP, BR-17033360 Bauru, SP - Brazil
[3] Corumba Concessoes SA, BR-71200030 Brasilia, DF - Brazil
Total Affiliations: 3
Document type: Journal article
Source: PATTERN RECOGNITION LETTERS; v. 128, p. 16-22, DEC 1 2019.
Web of Science Citations: 0

The annotation of large datasets is an issue whose challenge increases as the number of labeled samples available to train the classifier reduces in comparison to the amount of unlabeled data. In this context, semi-supervised learning methods aim at discovering and propagating labels to unlabeled samples, such that their correct labeling can improve the classification performance. In this work, we propose a semi-supervised methodology that explores the optimum connectivity among unlabeled samples through the Optimum-Path Forest (OPF) classifier to improve the learning process of Convolution Neural Networks (CNNs). Our proposal makes use of the OPF to classify an unlabeled training set that is used to pre-train a CNN for further fine-tuning using the limited labeled data only. The proposed approach is experimentally validated on traditional datasets and provides competitive results in comparison to state-of-the-art semi-supervised learning methods. (C) 2019 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
Grantee:Alexandre Xavier Falcão
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:José Alberto Cuminato
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 15/25739-4 - On the Study of Semantics in Deep Learning Models
Grantee:Gustavo Henrique de Rosa
Support Opportunities: Scholarships in Brazil - Master
FAPESP's process: 16/19403-6 - Energy-based learning models and their applications
Grantee:João Paulo Papa
Support Opportunities: Regular Research Grants