Rémi Flamary

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Je suis Maître de Conférence à l'Université de Nice Sophia-Antipolis au sein du département d'Électronique et du Laboratoire Lagrange. Ce laboratoire fait partie de l'Observatoire de la Côte d'Azur. J'ai auparavant préparé une thèse, sous la direction d'Alain Rakotomamonjy, à l'Université de Rouen et au Laboratoire LITIS.

Sur ce site web, vous trouverez une liste de mes publications, des supports de cours, ainsi que divers logiciels et code source.

Intérêts de recherche

  • Apprentissage statistique et traitement statistique du signal
    • Apprentissage supervisé, classification
    • Méthodes à noyaux, Séparateurs à Vaste Marge
    • Optimisation avec sélection de variable, normes mixtes, non-convexes
    • Apprentissage de représentations, apprentissage de noyaux
    • Réseaux de neurones convolutionels, filtrage, reconstruction d'image
    • Transport Optimal, adaptation de domaine
  • Applications
    • Classification de signaux biomédicaux, Interfaces Cerveaux-Machine
    • Télédétection et imagerie hyperspectrale
    • Traitement d'images astrophysiques

Nuage de mots-clés de mes intérêts de recherche.

Travaux récents

R. Flamary, "Astronomical image reconstruction with convolutional neural networks", European Conference on Signal Processing (EUSIPCO), 2017.
Abstract: State of the art methods in astronomical image reconstruction rely on the resolution of a regularized or constrained optimization problem. Solving this problem can be computationally intensive and usually leads to a quadratic or at least superlinear complexity w.r.t. the number of pixels in the image. We investigate in this work the use of convolutional neural networks for image reconstruction in astronomy. With neural networks, the computationally intensive tasks is the training step, but the prediction step has a fixed complexity per pixel, i.e. a linear complexity. Numerical experiments show that our approach is both computationally efficient and competitive with other state of the art methods in addition to being interpretable.
BibTeX:
@inproceedings{flamary2017astro,
author = {Flamary, Remi},
title = {Astronomical image reconstruction with convolutional neural networks},
booktitle = {European Conference on Signal Processing (EUSIPCO)},
year = {2017}
}
P. Hartley, R. Flamary, N. Jackson, A. S. Tagore, R. B. Metcalf, "Support Vector Machine classification of strong gravitational lenses", Monthly Notices of the Royal Astronomical Society (MNRAS), 2017.
Abstract: The imminent advent of very large-scale optical sky surveys, such as Euclid and LSST, makes it important to find efficient ways of discovering rare objects such as strong gravitational lens systems, where a background object is multiply gravitationally imaged by a foreground mass. As well as finding the lens systems, it is important to reject false positives due to intrinsic structure in galaxies, and much work is in progress with machine learning algorithms such as neural networks in order to achieve both these aims. We present and discuss a Support Vector Machine (SVM) algorithm which makes use of a Gabor filterbank in order to provide learning criteria for separation of lenses and non-lenses, and demonstrate using blind challenges that under certain circumstances it is a particularly efficient algorithm for rejecting false positives. We compare the SVM engine with a large-scale human examination of 100000 simulated lenses in a challenge dataset, and also apply the SVM method to survey images from the Kilo-Degree Survey.
BibTeX:
@article{hartley2017support,
author = {Hartley, Philippa, and Flamary, Remi and Jackson, Neal and Tagore, A. S. and Metcalf, R. B.},
title = {Support Vector Machine classification of strong gravitational lenses},
journal = {Monthly Notices of the Royal Astronomical Society (MNRAS)},
year = {2017}
}
R. Flamary, C. Févotte, N. Courty, V. Emyia, "Optimal spectral transportation with application to music transcription", Neural Information Processing Systems (NIPS), 2016.
Abstract: Many spectral unmixing methods rely on the non-negative decomposition of spectral data onto a dictionary of spectral templates. In particular, state-of-the-art music transcription systems decompose the spectrogram of the input signal onto a dictionary of representative note spectra. The typical measures of fit used to quantify the adequacy of the decomposition compare the data and template entries frequency-wise. As such, small displacements of energy from a frequency bin to another as well as variations of timber can disproportionally harm the fit. We address these issues by means of optimal transportation and propose a new measure of fit that treats the frequency distributions of energy holistically as opposed to frequency-wise. Building on the harmonic nature of sound, the new measure is invariant to shifts of energy to harmonically-related frequencies, as well as to small and local displacements of energy. Equipped with this new measure of fit, the dictionary of note templates can be considerably simplified to a set of Dirac vectors located at the target fundamental frequencies (musical pitch values). This in turns gives ground to a very fast and simple decomposition algorithm that achieves state-of-the-art performance on real musical data.
BibTeX:
@inproceedings{flamary2016ost,
author = {Flamary, Remi and Févotte, Cédric and Courty, N. and  Emyia, Valentin},
title = {Optimal spectral transportation with application to music transcription},
booktitle = { Neural Information Processing Systems (NIPS)},
year = {2016}
}
M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016.
Abstract: We are interested in the computation of the transport map of an Optimal Transport problem. Most of the computational approaches of Optimal Transport use the Kantorovich relaxation of the problem to learn a probabilistic coupling but do not address the problem of learning the transport map linked to the original Monge problem. Consequently, it lowers the potential usage of such methods in contexts where out-of-samples computations are mandatory. In this paper we propose a new way to jointly learn the coupling and an approximation of the transport map. We use a jointly convex formulation which can be efficiently optimized. Additionally, jointly learning the coupling and the transport map allows to smooth the result of the Optimal Transport and generalize it on out-of-samples examples. Empirically, we show the interest and the relevance of our method in two tasks: domain adaptation and image editing.
BibTeX:
@inproceedings{perrot2016mapping,
author = {Perrot, M. and Courty, N. and Flamary, R. and Habrard, A.},
title = {Mapping estimation for discrete optimal transport},
booktitle = {Neural Information Processing Systems (NIPS)},
year = {2016}
}
N. Courty, R. Flamary, D. Tuia, A. Rakotomamonjy, "Optimal transport for domain adaptation", Pattern Analysis and Machine Intelligence, IEEE Transactions on , 2016.
Abstract: Domain adaptation is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data representations become more robust when confronted to data depicting the same semantic concepts (the classes), but observed by another observation system with its own specificities. Among the many strategies proposed to adapt a domain to another, finding domain-invariant representations has shown excellent properties, as a single classifier can use labelled samples from the source domain under this representation to predict the unlabelled samples of the target domain. In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target domains. We learn a transportation plan matching both PDFs, which constrains labelled samples in the source domain to remain close during transport. This way, we exploit at the same time the few labeled information in the source and distributions of the input/observation variables observed in both domains. Experiments in toy and challenging real visual adaptation examples show the interest of the method, that consistently outperforms state of the art approaches.
BibTeX:
@article{courty2016optimal,
author = { Courty, N. and Flamary, R.  and Tuia, D. and Rakotomamonjy, A.},
title = {Optimal transport for domain adaptation},
journal = { Pattern Analysis and Machine Intelligence, IEEE Transactions on },
year = {2016}
}
S. Canu, R. Flamary, D. Mary, "Introduction to optimization with applications in astronomy and astrophysics", Mathematical tools for instrumentation and signal processing in astronomy, 2016.
Abstract: This chapter aims at providing an introduction to numerical optimization with some applications in astronomy and astrophysics. We provide important preliminary definitions that will guide the reader towards different optimization procedures. We discuss three families of optimization problems and describe numerical algorithms allowing, when this is possible, to solve these problems. For each family, we present in detail simple examples and more involved advanced examples. As a final illustration, we focus on two worked-out examples of optimization applied to astronomical data. The first application is a supervised classification of RR-Lyrae stars. The second one is the denoising of galactic spectra formulated by means of sparsity inducing models in a redundant dictionary.
BibTeX:
@incollection{canu2016introduction,
author = { Canu, Stephane, and Flamary, Remi and Mary, David},
title = {Introduction to optimization with applications in astronomy and astrophysics},
booktitle = { Mathematical tools for instrumentation and signal processing in astronomy},
editor = { {Mary, David and Flamary, Remi, and Theys, Celine, and Aime, Claude}},
year = {2016}
}
I. Harrane, R. Flamary, C. Richard, "Doubly partial-diffusion LMS over adaptive networks", Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2016.
Abstract: Diffusion LMS is an efficient strategy for solving distributed optimization problems with cooperating agents. Nodes are interested in estimating the same parameter vector and exchange information with their neighbors to improve their local estimates. However, successful implementation of such applications depends on a substantial amount of communication resources. In this paper, we introduce diffusion algorithms that have a significantly reduced communication load without compromising performance. We also perform analyses in the mean and mean-square sense. Simulations results are provided to confirm the theoretical findings.
BibTeX:
@inproceedings{harrane2016doubly,
author = {Harrane, Ibrahim and Flamary, R. and Richard, C.},
title = {Doubly partial-diffusion LMS over adaptive networks},
booktitle = {Asilomar Conference on Signals, Systems and Computers (ASILOMAR)},
year = {2016}
}
D. Tuia, R. Flamary, M. Barlaud, "Non-convex regularization in remote sensing", Geoscience and Remote Sensing, IEEE Transactions on, 2016.
Abstract: In this paper, we study the effect of different regularizers and their implications in high dimensional image classification and sparse linear unmixing. Although kernelization or sparse methods are globally accepted solutions for processing data in high dimensions, we present here a study on the impact of the form of regularization used and its parametrization. We consider regularization via traditional squared (l2) and sparsity-promoting (l1) norms, as well as more unconventional nonconvex regularizers (lp and Log Sum Penalty). We compare their properties and advantages on several classification and linear unmixing tasks and provide advices on the choice of the best regularizer for the problem at hand. Finally, we also provide a fully functional toolbox for the community
BibTeX:
@article{tuia2016nonconvex,
author = {Tuia, D. and  Flamary, R. and Barlaud, M.},
title = {Non-convex regularization in remote sensing},
journal = {Geoscience and Remote Sensing, IEEE Transactions on},
year = {2016}
}

News

POT Transport Optimal en Python

2016-11-07

Nous avons proposé une bibliothèque Python library pour le ransport optimal appelée POT. Elle est disponible sur Github et peut être installée directement à partir de PyPI. La bibliothèque implémente de nombreux solveurs liés au transport optimal dans la littérature de traitement d'image et de machine learning (voir le fichier README et la Documentation pour plus de détails).

Nous donnons également de nombreux examples d'utilisation sous la forme de scripts Python et de notebook Jupyter qui ne nécessitent pas d'avoir Python.

Voici une liste de notebooks illustrant POT:

Nhésitez pas à utiliser et à contribuer à POT.

Deux papiers en transport optimal acceptés à NIPS 2016

2016-08-04

Mes collaborateurs et moi avons eu deux papiers acceptés à NIPS 2016

R. Flamary, C. Févotte, N. Courty, V. Emyia, "Optimal spectral transportation with application to music transcription", Neural Information Processing Systems (NIPS), 2016.

Abstract: Many spectral unmixing methods rely on the non-negative decomposition of spectral data onto a dictionary of spectral templates. In particular, state-of-the-art music transcription systems decompose the spectrogram of the input signal onto a dictionary of representative note spectra. The typical measures of fit used to quantify the adequacy of the decomposition compare the data and template entries frequency-wise. As such, small displacements of energy from a frequency bin to another as well as variations of timber can disproportionally harm the fit. We address these issues by means of optimal transportation and propose a new measure of fit that treats the frequency distributions of energy holistically as opposed to frequency-wise. Building on the harmonic nature of sound, the new measure is invariant to shifts of energy to harmonically-related frequencies, as well as to small and local displacements of energy. Equipped with this new measure of fit, the dictionary of note templates can be considerably simplified to a set of Dirac vectors located at the target fundamental frequencies (musical pitch values). This in turns gives ground to a very fast and simple decomposition algorithm that achieves state-of-the-art performance on real musical data.
BibTeX:
@inproceedings{flamary2016ost,
author = {Flamary, Remi and Févotte, Cédric and Courty, N. and  Emyia, Valentin},
title = {Optimal spectral transportation with application to music transcription},
booktitle = { Neural Information Processing Systems (NIPS)},
editor = {},
year = {2016}
} 

M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016.

Abstract: We are interested in the computation of the transport map of an Optimal Transport problem. Most of the computational approaches of Optimal Transport use the Kantorovich relaxation of the problem to learn a probabilistic coupling but do not address the problem of learning the transport map linked to the original Monge problem. Consequently, it lowers the potential usage of such methods in contexts where out-of-samples computations are mandatory. In this paper we propose a new way to jointly learn the coupling and an approximation of the transport map. We use a jointly convex formulation which can be efficiently optimized. Additionally, jointly learning the coupling and the transport map allows to smooth the result of the Optimal Transport and generalize it on out-of-samples examples. Empirically, we show the interest and the relevance of our method in two tasks: domain adaptation and image editing.
BibTeX:
@inproceedings{perrot2016mapping,
author = {Perrot, M. and Courty, N. and Flamary, R. and Habrard, A.},
title = {Mapping estimation for discrete optimal transport},
booktitle = {Neural Information Processing Systems (NIPS)},
editor = {},
year = {2016}
} 

Ces deux papiers proposent des applications du transport optimal au machine learning et traitement du signal. N'hésitez pas à venir nous voir à nos posters, il y aura également des démonstrations en live d'annotation musicale et de copie transparente en image.

Prix Helava du meilleur papier dans le journal ISPRS sur la période 2012-2015

2016-07-06

Notre papier a été sélectionné pour le Prix Helava, du meilleur papier dans ISPRS Journal of Photogrammetry and Remote Sensing pour la période 2012-2015.

D. Tuia, R. Flamary, N. Courty, "Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions", ISPRS Journal of Photogrammetry and Remote Sensing, 2015.

Abstract: In this paper, we tackle the question of discovering an effective set of spatial filters to solve hyperspectral classification problems. Instead of fixing a priori the filters and their parameters using expert knowledge, we let the model find them within random draws in the (possibly infinite) space of possible filters. We define an active set feature learner that includes in the model only features that improve the classifier. To this end, we consider a fast and linear classifier, multiclass logistic classification, and show that with a good representation (the filters discovered), such a simple classifier can reach at least state of the art performances. We apply the proposed active set learner in four hyperspectral image classification problems, including agricultural and urban classification at different resolutions, as well as multimodal data. We also propose a hierarchical setting, which allows to generate more complex banks of features that can better describe the nonlinearities present in the data.
BibTeX:
@article{tuia2015multiclass,
author = {Tuia, D. and Flamary, R. and  Courty, N.},
title = {Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
editor = {},
year = {2015}
} 

C'est un grand honneur pour nous et je serais présent au ISPRS Congress 2016 le 12 Juillet 2016 pour recevoir le prix au nom de tous les auteurs. Ces travaux ont été effectués en collaboration avec Devis Tuia et Nicolas Courty.