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

B. B. Damodaran, B. Kellenberger, R. Flamary, D. Tuia, N. Courty, "DeepJDOT: Deep Joint distribution optimal transport for unsupervised domain adaptation", Europena Confernec in Computer Visions (ECCV), 2018.
Abstract: In computer vision, one is often confronted with problems of domain shifts, which occur when one applies a classifier trained on a source dataset to target data sharing similar characteristics (e.g. same classes), but also different latent data structures (e.g. different acquisition conditions). In such a situation, the model will perform poorly on the new data, since the classifier is specialized to recognize visual cues specific to the source domain. In this work we explore a solution, named DeepJDOT, to tackle this problem: through a measure of discrepancy on joint deep representations/labels based on optimal transport, we not only learn new data representations aligned between the source and target domain, but also simultaneously preserve the discriminative information used by the classifier. We applied DeepJDOT to a series of visual recognition tasks, where it compares favorably against state-of-the-art deep domain adaptation methods.
BibTeX:
@inproceedings{damodaran2018deepjdot,
author = { Damodaran, Bharath B. and Kellenberger, Benjamin and Flamary, Rémi and Tuia, Devis and Courty, Nicolas},
title = {DeepJDOT: Deep Joint distribution optimal transport for unsupervised domain adaptation},
booktitle = {Europena Confernec in Computer Visions (ECCV)},
year = {2018}
}
I. Harrane, R. Flamary, C. Richard, "On reducing the communication cost of the diffusion LMS algorithm", IEEE Transactions on Signal and Information Processing over Networks (SIPN), 2018.
Abstract: The rise of digital and mobile communications has recently made the world more connected and networked, resulting in an unprecedented volume of data flowing between sources, data centers, or processes. While these data may be processed in a centralized manner, it is often more suitable to consider distributed strategies such as diffusion as they are scalable and can handle large amounts of data by distributing tasks over networked agents. Although it is relatively simple to implement diffusion strategies over a cluster, it appears to be challenging to deploy them in an ad-hoc network with limited energy budget for communication. In this paper, we introduce a diffusion LMS strategy that significantly reduces communication costs without compromising the performance. Then, we analyze the proposed algorithm in the mean and mean-square sense. Next, we conduct numerical experiments to confirm the theoretical findings. Finally, we perform large scale simulations to test the algorithm efficiency in a scenario where energy is limited.
BibTeX:
@article{harrane2018reducing,
author = {Harrane, Ibrahim and Flamary, R. and Richard, C.},
title = {On reducing the communication cost of the diffusion LMS algorithm},
journal = {IEEE Transactions on Signal and Information Processing over Networks (SIPN)},
year = {2018}
}
R. Flamary, M. Cuturi, N. Courty, A. Rakotomamonjy, "Wasserstein Discriminant Analysis", Machine learning , 2018.
Abstract: Wasserstein Discriminant Analysis (WDA) is a new supervised method that can improve classification of high-dimensional data by computing a suitable linear map onto a lower dimensional subspace. Following the blueprint of classical Linear Discriminant Analysis (LDA), WDA selects the projection matrix that maximizes the ratio of two quantities: the dispersion of projected points coming from different classes, divided by the dispersion of projected points coming from the same class. To quantify dispersion, WDA uses regularized Wasserstein distances, rather than cross-variance measures which have been usually considered, notably in LDA. Thanks to the the underlying principles of optimal transport, WDA is able to capture both global (at distribution scale) and local (at samples scale) interactions between classes. Regularized Wasserstein distances can be computed using the Sinkhorn matrix scaling algorithm; We show that the optimization of WDA can be tackled using automatic differentiation of Sinkhorn iterations. Numerical experiments show promising results both in terms of prediction and visualization on toy examples and real life datasets such as MNIST and on deep features obtained from a subset of the Caltech dataset.
BibTeX:
@article{flamary2017wasserstein,
author = {Flamary, Remi and Cuturi, Marco and Courty, Nicolas and Rakotomamonjy, Alain},
title = {Wasserstein Discriminant Analysis},
journal = { Machine learning },
year = {2018}
}
N. Courty, R. Flamary, M. Ducoffe, "Learning Wasserstein Embeddings", International Conference on Learning Representations (ICLR), 2018.
Abstract: The Wasserstein distance received a lot of attention recently in the community of machine learning, especially for its principled way of comparing distributions. It has found numerous applications in several hard problems, such as domain adaptation, dimensionality reduction or generative models. However, its use is still limited by a heavy computational cost. Our goal is to alleviate this problem by providing an approximation mechanism that allows to break its inherent complexity. It relies on the search of an embedding where the Euclidean distance mimics the Wasserstein distance. We show that such an embedding can be found with a siamese architecture associated with a decoder network that allows to move from the embedding space back to the original input space. Once this embedding has been found, computing optimization problems in the Wasserstein space (e.g. barycenters, principal directions or even archetypes) can be conducted extremely fast. Numerical experiments supporting this idea are conducted on image datasets, and show the wide potential benefits of our method.
BibTeX:
@inproceedings{courty2018learning,
author = {Courty, Nicolas and Flamary, Remi and Ducoffe, Melanie},
title = {Learning Wasserstein Embeddings},
booktitle = {International Conference on Learning Representations (ICLR)},
year = {2018}
}
V. Seguy, B. B. Damodaran, R. Flamary, N. Courty, A. Rolet, M. Blondel, "Large-Scale Optimal Transport and Mapping Estimation", International Conference on Learning Representations (ICLR), 2018.
Abstract: This paper presents a novel two-step approach for the fundamental problem of learning an optimal map from one distribution to another. First, we learn an optimal transport (OT) plan, which can be thought as a one-to-many map between the two distributions. To that end, we propose a stochastic dual approach of regularized OT, and show empirically that it scales better than a recent related approach when the amount of samples is very large. Second, we estimate a Monge map as a deep neural network learned by approximating the barycentric projection of the previously-obtained OT plan. We prove two theoretical stability results of regularized OT which show that our estimations converge to the OT plan and Monge map between the underlying continuous measures. We showcase our proposed approach on two applications: domain adaptation and generative modeling.
BibTeX:
@inproceedings{seguy2018large,
author = {Seguy, Vivien. and Damodaran, Bharath B.  and Flamary, Remi and Courty, Nicolas and Rolet, Antoine and Blondel, Mathieu},
title = {Large-Scale Optimal Transport and Mapping Estimation},
booktitle = {International Conference on Learning Representations (ICLR)},
year = {2018}
}
N. Courty, R. Flamary, A. Habrard, A. Rakotomamonjy, "Joint Distribution Optimal Transportation for Domain Adaptation", Neural Information Processing Systems (NIPS), 2017.
Abstract: This paper deals with the unsupervised domain adaptation problem, where one wants to estimate a prediction function f in a given target domain without any labeled sample by exploiting the knowledge available from a source domain where labels are known. Our work makes the following assumption: there exists a non-linear transformation between the joint feature/label space distributions of the two domain Ps and Pt. We propose a solution of this problem with optimal transport, that allows to recover an estimated target Pft(X,f(X)) by optimizing simultaneously the optimal coupling and f. We show that our method corresponds to the minimization of a bound on the target error, and provide an efficient algorithmic solution, for which convergence is proved. The versatility of our approach, both in terms of class of hypothesis or loss functions is demonstrated with real world classification and regression problems, for which we reach or surpass state-of-the-art results.
BibTeX:
@inproceedings{courty2017joint,
author = {Courty, Nicolas and Flamary, Remi and Habrard, Amaury and Rakotomamonjy, Alain},
title = {Joint Distribution Optimal Transportation for Domain Adaptation},
booktitle = {Neural Information Processing Systems (NIPS)},
year = {2017}
}
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}
}

News

Optimal Transport à Data Science Summer School 2018

2018-06-19

Nous allons donner avec Marco Cuturi et Nicolas Courty deux cours d'une journée sur le Transport optimal appliqué à l'apprentissage statistique pour l'école d'été Data Science Summer School 2018 (DS3) à l'École Polytechnique à Paris/Saclay, France.

Vous pouvez trouver les présentations ainsi que les notebook Python pour la séances de travaux pratiques sur Github.

Optimal Transport à Statlearn 2018

2018-04-05

Nous avons donné avec Nicolas Courty un cours d'une journée sur le Transport optimal appliqué à l'apprentissage statistique pour l'école de printemps Statlearn 2018 à Nice, France.

Vous pouvez trouver les présentations ainsi que les notebook Python pour la séances de travaux pratiques sur Github.

Présentation à l'assemblée générale du GDR ISIS

2017-11-17

J'ai eu l'honeur d'être invité à présenter mes travaux là l'AG du GDR ISIS à Sète.

J'ai présenté un bref rappel de la théorie et des outils du transport optimal, suivi par un panorama des avancées récentes liées à l'utilisation du transport optimal en apprentissage statistique et en estimation. La présentation est disponible ici (diapos en anglais)