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Towards the Robustness in Deep Learning Architectures for e-Science Applications

Grant number: 18/23908-1
Support Opportunities:Scholarships abroad - Research
Effective date (Start): July 07, 2019
Effective date (End): July 06, 2020
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Fabio Augusto Faria
Grantee:Fabio Augusto Faria
Host Investigator: Gustavo Carneiro
Host Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil
Research place: University of Adelaide, Australia  

Abstract

Deep learning architectures, in particular the convolutional neural networks (CNNs) are responsible for recent research advances in computational vision and machine learning areas. Due to the fact these networks have achieved excellent results in different application domains. The resurgence of CNNs occurred in 2012 when the proposed AlexNet architecture managed to reduce the error rate of the ImageNet competition by 10% in the Large Scale Visual Recognition Challenge (ILSVRC). This surprising result attracted scientific community views for those deep learning networks and in a short time, the error rate dropped to 7.3% and nowadays the challenge is virtually solved with error rates of less than 3%. In 2014, a Google research group found that several machine learning models were vulnerable to adversarial examples. The addition of imperceptible noise in images was enough to fool any trained machine learning model. This fact has leveraged a new research field, adversarial pattern recognition, which aims the creation of robust learning models to data distribution different from used in the training process (adversarial examples). This undesired behavior of CNNs, observed in the literature, might be caused by problems occurred at various moments throughout the learning process. In this sense, this research project aims to study, evaluate and develop new deep learning approaches more robust to adversarial examples and thus improve the effectiveness results in the multimedia data classification tasks in real applications of the e-Science domain.

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Scientific publications (8)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
FERREIRA, ALVARO R., JR.; DE ROSA, GUSTAVO H.; PAPA, JOAO P.; CARNEIRO, GUSTAVO; FARIA, FABIO A.; IEEE COMP SOC. Creating Classifier Ensembles through Meta-heuristic Algorithms for Aerial Scene Classification. 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), v. N/A, p. 8-pg., . (14/12236-1, 18/23908-1, 17/25908-6, 19/07665-4, 19/02205-5)
AONO, ALEXANDRE H.; NAGAI, JAMES S.; DICKEL, GABRIELLA DA S. M.; MARINHO, RAFAELA C.; DE OLIVEIRA, PAULO E. A. M.; PAPA, JOAO P.; FARIA, FABIO A.. stomata classification and detection system in microscope images of maize cultivar. PLoS One, v. 16, n. 10, . (18/23908-1)
PIMENTA, GUILHERME B. A.; DALLAQUA, FERNANDA B. J. R.; FAZENDA, ALVARO; FARIA, FABIO A.; DECARVALHO, BM; GONCALVES, LMG. Neuroevolution-based Classifiers for Deforestation Detection in Tropical Forests. 2022 35TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2022), v. N/A, p. 6-pg., . (19/26702-8, 15/24485-9, 14/50937-1, 18/23908-1, 17/25908-6)
FARIA, FABIO AUGUSTO; CARNEIRO, GUSTAVO; IEEE. Why are Generative Adversarial Networks so Fascinating and Annoying?. 2020 33RD SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2020), v. N/A, p. 8-pg., . (18/23908-1, 17/25908-6)
BURIS, LUIZ H.; PEDRONETTE, DANIEL C. G.; PAPA, JOAO P.; ALMEIDA, JURANDY; CARNEIRO, GUSTAVO; FARIA, FABIO A.; IEEE. MIXUP-BASED DEEP METRIC LEARNING APPROACHES FOR INCOMPLETE SUPERVISION. 2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, v. N/A, p. 5-pg., . (18/23908-1, 14/12236-1, 19/07665-4, 21/01870-5)
PRESOTTO, JOAO GABRIEL CAMACHO; DOS SANTOS, SAMUEL FELIPE; VALEM, LUCAS PASCOTTI; FARIA, FABIO AUGUSTO; PAPA, JOAO PAULO; ALMEIDA, JURANDY; PEDRONETTE, DANIEL CARLOS GUIMARAES. Weakly supervised learning based on hypergraph manifold ranking?. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, v. 89, p. 12-pg., . (18/15597-6, 18/23908-1, 17/25908-6, 19/04754-6, 20/11366-0)
AONO, ALEXANDRE H.; NAGAI, JAMES S.; DICKEL, GABRIELLA DA S. M.; MARINHO, RAFAELA C.; DE OLIVEIRA, PAULO E. A. M.; PAPA, JOAO P.; FARIA, FABIO A.. A stomata classification and detection system in microscope images of maize cultivars. PLoS One, v. 16, n. 10, p. 17-pg., . (18/23908-1)
ANDRADE, NATAN; FARIA, FABIO A.; CAPPABIANCO, FABIO A. M.; IEEE COMP SOC. Improving Similarity Metric of Multi-modal MR Brain Image Registration Via a Deep Ensemble. 2021 34TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2021), v. N/A, p. 8-pg., . (18/23908-1, 16/21591-5)

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