Search Results for author: Nicolas Courty

Found 53 papers, 33 papers with code

Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein

no code implementations5 Oct 2023 Hugues van Assel, Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Nicolas Courty

We present a versatile adaptation of existing dimensionality reduction (DR) objectives, enabling the simultaneous reduction of both sample and feature sizes.

Clustering Dimensionality Reduction

Optimal Transport with Adaptive Regularisation

no code implementations4 Oct 2023 Hugues van Assel, Titouan Vayer, Remi Flamary, Nicolas Courty

Regularising the primal formulation of optimal transport (OT) with a strictly convex term leads to enhanced numerical complexity and a denser transport plan.

Domain Adaptation

Fast Optimal Transport through Sliced Wasserstein Generalized Geodesics

1 code implementation4 Jul 2023 Guillaume Mahey, Laetitia Chapel, Gilles Gasso, Clément Bonet, Nicolas Courty

Wasserstein distance (WD) and the associated optimal transport plan have been proven useful in many applications where probability measures are at stake.

Colorization Image Colorization

Joint multi-modal Self-Supervised pre-training in Remote Sensing: Application to Methane Source Classification

no code implementations16 Jun 2023 Paul Berg, Minh-Tan Pham, Nicolas Courty

In earth observation, there are opportunities to exploit domain-specific remote sensing image data in order to improve these methods.

Unbalanced Optimal Transport meets Sliced-Wasserstein

no code implementations12 Jun 2023 Thibault Séjourné, Clément Bonet, Kilian Fatras, Kimia Nadjahi, Nicolas Courty

In parallel, unbalanced OT was designed to allow comparisons of more general positive measures, while being more robust to outliers.

SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation

1 code implementation6 Apr 2023 Bjoern Michele, Alexandre Boulch, Gilles Puy, Tuan-Hung Vu, Renaud Marlet, Nicolas Courty

Learning models on one labeled dataset that generalize well on another domain is a difficult task, as several shifts might happen between the data domains.

Semantic Segmentation Unsupervised Domain Adaptation

Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals

2 code implementations10 Mar 2023 Clément Bonet, Benoît Malézieux, Alain Rakotomamonjy, Lucas Drumetz, Thomas Moreau, Matthieu Kowalski, Nicolas Courty

When dealing with electro or magnetoencephalography records, many supervised prediction tasks are solved by working with covariance matrices to summarize the signals.

Brain Computer Interface Computational Efficiency +5

Hyperbolic Sliced-Wasserstein via Geodesic and Horospherical Projections

1 code implementation18 Nov 2022 Clément Bonet, Laetitia Chapel, Lucas Drumetz, Nicolas Courty

It has been shown beneficial for many types of data which present an underlying hierarchical structure to be embedded in hyperbolic spaces.

Image Classification

Turning Normalizing Flows into Monge Maps with Geodesic Gaussian Preserving Flows

1 code implementation22 Sep 2022 Guillaume Morel, Lucas Drumetz, Simon Benaïchouche, Nicolas Courty, François Rousseau

Normalizing Flows (NF) are powerful likelihood-based generative models that are able to trade off between expressivity and tractability to model complex densities.

Spherical Sliced-Wasserstein

1 code implementation17 Jun 2022 Clément Bonet, Paul Berg, Nicolas Courty, François Septier, Lucas Drumetz, Minh-Tan Pham

Many variants of the Wasserstein distance have been introduced to reduce its original computational burden.

Density Estimation Variational Inference

Template based Graph Neural Network with Optimal Transport Distances

1 code implementation31 May 2022 Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty

Current Graph Neural Networks (GNN) architectures generally rely on two important components: node features embedding through message passing, and aggregation with a specialized form of pooling.

Graph Classification Graph Matching

Unbalanced CO-Optimal Transport

no code implementations30 May 2022 Quang Huy Tran, Hicham Janati, Nicolas Courty, Rémi Flamary, Ievgen Redko, Pinar Demetci, Ritambhara Singh

With this result in hand, we provide empirical evidence of this robustness for the challenging tasks of heterogeneous domain adaptation with and without varying proportions of classes and simultaneous alignment of samples and features across single-cell measurements.

Domain Adaptation

Subspace Detours Meet Gromov-Wasserstein

no code implementations21 Oct 2021 Clément Bonet, Nicolas Courty, François Septier, Lucas Drumetz

In the context of optimal transport methods, the subspace detour approach was recently presented by Muzellec and Cuturi (2019).

Efficient Gradient Flows in Sliced-Wasserstein Space

1 code implementation21 Oct 2021 Clément Bonet, Nicolas Courty, François Septier, Lucas Drumetz

However, it requires solving a nested optimization problem at each iteration, and is known for its computational challenges, especially in high dimension.

Bayesian Inference Image Generation

Semi-relaxed Gromov-Wasserstein divergence with applications on graphs

1 code implementation6 Oct 2021 Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty

To this end, the Gromov-Wasserstein (GW) distance, based on Optimal Transport (OT), has proven to be successful in handling the specific nature of the associated objects.

Dictionary Learning

Factored couplings in multi-marginal optimal transport via difference of convex programming

no code implementations1 Oct 2021 Quang Huy Tran, Hicham Janati, Ievgen Redko, Rémi Flamary, Nicolas Courty

Optimal transport (OT) theory underlies many emerging machine learning (ML) methods nowadays solving a wide range of tasks such as generative modeling, transfer learning and information retrieval.

Information Retrieval Retrieval +1

Semi-relaxed Gromov-Wasserstein divergence and applications on graphs

no code implementations ICLR 2022 Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty

To this end, the Gromov-Wasserstein (GW) distance, based on Optimal Transport (OT), has proven to be successful in handling the specific nature of the associated objects.

Dictionary Learning

Unbalanced minibatch Optimal Transport; applications to Domain Adaptation

2 code implementations5 Mar 2021 Kilian Fatras, Thibault Séjourné, Nicolas Courty, Rémi Flamary

Optimal transport distances have found many applications in machine learning for their capacity to compare non-parametric probability distributions.

Domain Adaptation

Learning to Generate Wasserstein Barycenters

1 code implementation24 Feb 2021 Julien Lacombe, Julie Digne, Nicolas Courty, Nicolas Bonneel

Wasserstein barycenters -- the problem of finding measures in-between given input measures in the optimal transport sense -- is even more computationally demanding as it requires to solve an optimization problem involving optimal transport distances.

Online Graph Dictionary Learning

1 code implementation12 Feb 2021 Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Marco Corneli, Nicolas Courty

Dictionary learning is a key tool for representation learning, that explains the data as linear combination of few basic elements.

Dictionary Learning Graph Classification +2

Minibatch optimal transport distances; analysis and applications

2 code implementations5 Jan 2021 Kilian Fatras, Younes Zine, Szymon Majewski, Rémi Flamary, Rémi Gribonval, Nicolas Courty

We notably argue that the minibatch strategy comes with appealing properties such as unbiased estimators, gradients and a concentration bound around the expectation, but also with limits: the minibatch OT is not a distance.

Contextual Semantic Interpretability

1 code implementation18 Sep 2020 Diego Marcos, Ruth Fong, Sylvain Lobry, Remi Flamary, Nicolas Courty, Devis Tuia

Once the attributes are learned, they can be re-combined to reach the final decision and provide both an accurate prediction and an explicit reasoning behind the CNN decision.

Representation Transfer by Optimal Transport

no code implementations13 Jul 2020 Xuhong Li, Yves GRANDVALET, Rémi Flamary, Nicolas Courty, Dejing Dou

We use optimal transport to quantify the match between two representations, yielding a distance that embeds some invariances inherent to the representation of deep networks.

Knowledge Distillation Model Compression +1

Optimal Transport for Conditional Domain Matching and Label Shift

1 code implementation15 Jun 2020 Alain Rakotomamonjy, Rémi Flamary, Gilles Gasso, Mokhtar Z. Alaya, Maxime Berar, Nicolas Courty

We address the problem of unsupervised domain adaptation under the setting of generalized target shift (joint class-conditional and label shifts).

Unsupervised Domain Adaptation

A Cycle GAN Approach for Heterogeneous Domain Adaptation in Land Use Classification

no code implementations22 Apr 2020 Claire Voreiter, Jean-Christophe Burnel, Pierre Lassalle, Marc Spigai, Romain Hugues, Nicolas Courty

In the field of remote sensing and more specifically in Earth Observation, new data are available every day, coming from different sensors.

Classification Domain Adaptation +1

CO-Optimal Transport

1 code implementation NeurIPS 2020 Ievgen Redko, Titouan Vayer, Rémi Flamary, Nicolas Courty

Optimal transport (OT) is a powerful geometric and probabilistic tool for finding correspondences and measuring similarity between two distributions.

Clustering Data Summarization +1

Generating Natural Adversarial Hyperspectral examples with a modified Wasserstein GAN

no code implementations27 Jan 2020 Jean-Christophe Burnel, Kilian Fatras, Nicolas Courty

In this paper, we present a new method which is able to generate natural adversarial examples from the true data following the second paradigm.

Learning with minibatch Wasserstein : asymptotic and gradient properties

3 code implementations9 Oct 2019 Kilian Fatras, Younes Zine, Rémi Flamary, Rémi Gribonval, Nicolas Courty

Optimal transport distances are powerful tools to compare probability distributions and have found many applications in machine learning.

Sliced Gromov-Wasserstein

1 code implementation NeurIPS 2019 Titouan Vayer, Rémi Flamary, Romain Tavenard, Laetitia Chapel, Nicolas Courty

Recently used in various machine learning contexts, the Gromov-Wasserstein distance (GW) allows for comparing distributions whose supports do not necessarily lie in the same metric space.

Wasserstein Adversarial Regularization (WAR) on label noise

1 code implementation8 Apr 2019 Kilian Fatras, Bharath Bhushan Damodaran, Sylvain Lobry, Rémi Flamary, Devis Tuia, Nicolas Courty

Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping.

Semantic Segmentation

End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping

2 code implementations30 Jan 2019 Marc Rußwurm, Nicolas Courty, Rémi Emonet, Sébastien Lefèvre, Devis Tuia, Romain Tavenard

In this work, we present an End-to-End Learned Early Classification of Time Series (ELECTS) model that estimates a classification score and a probability of whether sufficient data has been observed to come to an early and still accurate decision.

Classification Crop Classification +6

Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties

1 code implementation7 Nov 2018 Titouan Vayer, Laetita Chapel, Rémi Flamary, Romain Tavenard, Nicolas Courty

Optimal transport theory has recently found many applications in machine learning thanks to its capacity for comparing various machine learning objects considered as distributions.

BIG-bench Machine Learning

Optimal Transport for structured data with application on graphs

2 code implementations23 May 2018 Titouan Vayer, Laetitia Chapel, Rémi Flamary, Romain Tavenard, Nicolas Courty

This work considers the problem of computing distances between structured objects such as undirected graphs, seen as probability distributions in a specific metric space.

Clustering Graph Classification +2

DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation

4 code implementations ECCV 2018 Bharath Bhushan Damodaran, Benjamin Kellenberger, Rémi Flamary, Devis Tuia, Nicolas Courty

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).

Unsupervised Domain Adaptation

Optimal Transport for Multi-source Domain Adaptation under Target Shift

3 code implementations13 Mar 2018 Ievgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia

In this paper, we propose to tackle the problem of reducing discrepancies between multiple domains referred to as multi-source domain adaptation and consider it under the target shift assumption: in all domains we aim to solve a classification problem with the same output classes, but with labels' proportions differing across them.

Domain Adaptation Image Segmentation +1

Distance Measure Machines

no code implementations1 Mar 2018 Alain Rakotomamonjy, Abraham Traoré, Maxime Berar, Rémi Flamary, Nicolas Courty

This paper presents a distance-based discriminative framework for learning with probability distributions.

Data Dependent Kernel Approximation using Pseudo Random Fourier Features

no code implementations27 Nov 2017 Bharath Bhushan Damodaran, Nicolas Courty, Philippe-Henri Gosselin

Thus, reducing the number of feature dimensions is necessary to effectively scale to large datasets.

Large-Scale Optimal Transport and Mapping Estimation

2 code implementations7 Nov 2017 Vivien Seguy, Bharath Bhushan Damodaran, Rémi Flamary, Nicolas Courty, Antoine Rolet, Mathieu Blondel

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.

Domain Adaptation

Learning Wasserstein Embeddings

1 code implementation ICLR 2018 Nicolas Courty, Rémi Flamary, Mélanie Ducoffe

Our goal is to alleviate this problem by providing an approximation mechanism that allows to break its inherent complexity.

Dimensionality Reduction Domain Adaptation

Joint Distribution Optimal Transportation for Domain Adaptation

2 code implementations NeurIPS 2017 Nicolas Courty, Rémi Flamary, Amaury Habrard, Alain Rakotomamonjy

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.

Unsupervised Domain Adaptation

Mapping Estimation for Discrete Optimal Transport

no code implementations NeurIPS 2016 Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard

Most of the computational approaches of Optimal Transport use the Kantorovich relaxation of the problem to learn a probabilistic coupling $\mgamma$ but do not address the problem of learning the underlying transport map $\funcT$ linked to the original Monge problem.

Domain Adaptation

Optimal spectral transportation with application to music transcription

1 code implementation NeurIPS 2016 Rémi Flamary, Cédric Févotte, Nicolas Courty, Valentin Emiya

Many spectral unmixing methods rely on the non-negative decomposition of spectral data onto a dictionary of spectral templates.

Music Transcription

Wasserstein Discriminant Analysis

1 code implementation29 Aug 2016 Rémi Flamary, Marco Cuturi, Nicolas Courty, Alain Rakotomamonjy

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.

Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions

no code implementations23 Jun 2016 Devis Tuia, Rémi Flamary, Nicolas Courty

In this paper, we tackle the question of discovering an effective set of spatial filters to solve hyperspectral classification problems.

Classification General Classification +1

Generalized conditional gradient: analysis of convergence and applications

no code implementations22 Oct 2015 Alain Rakotomamonjy, Rémi Flamary, Nicolas Courty

The objectives of this technical report is to provide additional results on the generalized conditional gradient methods introduced by Bredies et al. [BLM05].

Optimal Transport for Domain Adaptation

no code implementations2 Jul 2015 Nicolas Courty, Rémi Flamary, Devis Tuia, Alain Rakotomamonjy

Domain adaptation from one data space (or domain) to another is one of the most challenging tasks of modern data analytics.

Domain Adaptation

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