Rémi Flamary

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I am associate professor at Nice-Sophia Antipolis University in the Department of Electronics and in the Lagrange Laboratory. This laboratory is part of the Observatoire de la Côte d'Azur. I was previously a PhD student and teaching assistant at the LITIS Laboratory and my PhD advisor was Alain Rakotomamonjy at Rouen University.

On this website, you can find a list of my publications and download the corresponding software/code. Some of my french teaching material is also available.

Research Interests

  • Machine learning and statistical signal processing
    • Classification, supervised learning
    • Kernel methods, Support Vector Machines
    • Optimization with sparsity, variable selection, mixed norms, non convex regularization
    • Feature learning, data representation, kernel learning
    • Convolutional neural networks, filter learning, image reconstruction
    • Optimal transport, domain adaptation
  • Applications
    • Biomedical engineering, Brain-Computer Interfaces
    • Remote sensing and hyperspectral Imaging
    • Astronomical image processing

Wordcloud of my research interests.

Recent work

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}
}
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

Talk at GDR ISIS General Meeting

2017-11-17

I had the honor to be invited for a talk at the GDR ISI General meeting in Sète.

I presented a short introduction to optimal transport and discussed some recent applications of OT to in the machine learning comunity. the slides in english are available here

Domain adaptation paper accepted at NIPS 2017 and OTML 2017 Workshop

2017-09-17

My collaborators and I have been accepted to present the following paper at NIPS 2017

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)},
editor = {},
year = {2017}
} 

I have been invited to present at the OTML 2017 Workshop and we also have two additional posters there.

Feel free to come and see us at our NIPS poster or at the workshop.

POT Python Optimal Transport library

2016-11-07

We proposed recently a general purpose Python library for Optimal Transport called POT. The library is available on Github and can be easily installed using PyPI. The toolbox implement a number of solvers from the image and machine learning literature (see README and the Documentation for more details).

We also give several examples of the potential uses of OT in the form of Python scripts and Python notebook that show the toolbox in use without requiring Python.

Here is a list of the Python notebooks if you want a quick look:

Feel free to use and contribute to the library.