no code implementations • pproximateinference AABI Symposium 2019 • Micael Carvalho, Thibaut Durand, JiaWei He, Nazanin Mehrasa, Greg Mori
In this paper, we propose an arbitrarily-conditioned data imputation framework built upon variational autoencoders and normalizing flows.
1 code implementation • 2 May 2018 • Micael Carvalho, Rémi Cadène, David Picard, Laure Soulier, Matthieu Cord
Recent advances in the machine learning community allowed different use cases to emerge, as its association to domains like cooking which created the computational cuisine.
1 code implementation • 30 Apr 2018 • Micael Carvalho, Rémi Cadène, David Picard, Laure Soulier, Nicolas Thome, Matthieu Cord
Designing powerful tools that support cooking activities has rapidly gained popularity due to the massive amounts of available data, as well as recent advances in machine learning that are capable of analyzing them.
Ranked #9 on Cross-Modal Retrieval on Recipe1M
no code implementations • 22 Jul 2017 • Julian Zilly, Amit Boyarski, Micael Carvalho, Amir Atapour Abarghouei, Konstantinos Amplianitis, Aleksandr Krasnov, Massimiliano Mancini, Hernán Gonzalez, Riccardo Spezialetti, Carlos Sampedro Pérez, Hao Li
Reviewing this project with modern eyes provides us with the opportunity to reflect on several issues, relevant now as then to the field of computer vision and research in general, that go beyond the technical aspects of the work.
1 code implementation • 11 May 2016 • Micael Carvalho, Matthieu Cord, Sandra Avila, Nicolas Thome, Eduardo Valle
In recent years, deep architectures have been used for transfer learning with state-of-the-art performance in many datasets.
2 code implementations • 14 Apr 2016 • Michel Fornaciali, Micael Carvalho, Flávia Vasques Bittencourt, Sandra Avila, Eduardo Valle
In this paper we survey, analyze and criticize current art on automated melanoma screening, reimplementing a baseline technique, and proposing two novel ones.