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Found 54 papers, 34 papers with code
Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds
On Euclidean spaces, a popular alternative is the Sliced-Wasserstein distance, which leverages a closed-form solution of the Wasserstein distance in one dimension, but which is not readily available on manifolds.
Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein Projection
Unsupervised learning aims to capture the underlying structure of potentially large and high-dimensional datasets.
Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein
We present a versatile adaptation of existing dimensionality reduction (DR) objectives, enabling the simultaneous reduction of both sample and feature sizes.
Optimal Transport with Adaptive Regularisation
Regularising the primal formulation of optimal transport (OT) with a strictly convex term leads to enhanced numerical complexity and a denser transport plan.
Match-And-Deform: Time Series Domain Adaptation through Optimal Transport and Temporal Alignment
While large volumes of unlabeled data are usually available, associated labels are often scarce.
Fast Optimal Transport through Sliced Wasserstein Generalized Geodesics
Wasserstein distance (WD) and the associated optimal transport plan have been proven useful in many applications where probability measures are at stake.
Joint multi-modal Self-Supervised pre-training in Remote Sensing: Application to Methane Source Classification
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
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
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.
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals
When dealing with electro or magnetoencephalography records, many supervised prediction tasks are solved by working with covariance matrices to summarize the signals.
Hyperbolic Sliced-Wasserstein via Geodesic and Horospherical Projections
It has been shown beneficial for many types of data which present an underlying hierarchical structure to be embedded in hyperbolic spaces.
Turning Normalizing Flows into Monge Maps with Geodesic Gaussian Preserving Flows
Normalizing Flows (NF) are powerful likelihood-based generative models that are able to trade off between expressivity and tractability to model complex densities.
Aligning individual brains with Fused Unbalanced Gromov-Wasserstein
We demonstrate that FUGW is well-suited for whole-brain landmark-free alignment.
Spherical Sliced-Wasserstein
Many variants of the Wasserstein distance have been introduced to reduce its original computational burden.
Template based Graph Neural Network with Optimal Transport Distances
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.
Ranked #1 on
Graph Classification
on NCI1
Unbalanced CO-Optimal Transport
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.
Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport
Training data are usually limited or heterogeneous in many chemical and biological applications.
Efficient Gradient Flows in Sliced-Wasserstein Space
However, it requires solving a nested optimization problem at each iteration, and is known for its computational challenges, especially in high dimension.
Subspace Detours Meet Gromov-Wasserstein
In the context of optimal transport methods, the subspace detour approach was recently presented by Muzellec and Cuturi (2019).
Semi-relaxed Gromov-Wasserstein divergence with applications on graphs
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.
Factored couplings in multi-marginal optimal transport via difference of convex programming
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.
Semi-relaxed Gromov-Wasserstein divergence and applications on graphs
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.
Unbalanced minibatch Optimal Transport; applications to Domain Adaptation
Optimal transport distances have found many applications in machine learning for their capacity to compare non-parametric probability distributions.
Learning to Generate Wasserstein Barycenters
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
Dictionary learning is a key tool for representation learning, that explains the data as linear combination of few basic elements.
Ranked #1 on
Graph Classification
on BZR
Minibatch optimal transport distances; analysis and applications
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
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
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.
Optimal Transport for Conditional Domain Matching and Label Shift
We address the problem of unsupervised domain adaptation under the setting of generalized target shift (joint class-conditional and label shifts).
A Cycle GAN Approach for Heterogeneous Domain Adaptation in Land Use Classification
In the field of remote sensing and more specifically in Earth Observation, new data are available every day, coming from different sensors.
CO-Optimal Transport
Optimal transport (OT) is a powerful geometric and probabilistic tool for finding correspondences and measuring similarity between two distributions.
Time Series Alignment with Global Invariances
Multivariate time series are ubiquitous objects in signal processing.
Generating Natural Adversarial Hyperspectral examples with a modified Wasserstein GAN
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
Optimal transport distances are powerful tools to compare probability distributions and have found many applications in machine learning.
Sliced Gromov-Wasserstein
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
Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping.
End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping
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.
Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties
Optimal transport theory has recently found many applications in machine learning thanks to its capacity for comparing various machine learning objects considered as distributions.
An Entropic Optimal Transport Loss for Learning Deep Neural Networks under Label Noise in Remote Sensing Images
The state-of-the-art performances of deep neural networks are conditioned to the availability of large number of accurately labeled samples.
Optimal Transport for structured data with application on graphs
This work considers the problem of computing distances between structured objects such as undirected graphs, seen as probability distributions in a specific metric space.
Ranked #3 on
Graph Classification
on NCI1
DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation
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).
Ranked #2 on
Domain Adaptation
on MNIST-to-MNIST-M
Optimal Transport for Multi-source Domain Adaptation under Target Shift
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.
Distance Measure Machines
This paper presents a distance-based discriminative framework for learning with probability distributions.
Large Scale Optimal Transport and Mapping Estimation
First, we learn an optimal transport (OT) plan, which can be thought as a one-to-many map between the two distributions.
Data Dependent Kernel Approximation using Pseudo Random Fourier Features
Thus, reducing the number of feature dimensions is necessary to effectively scale to large datasets.
Large-Scale Optimal Transport and Mapping Estimation
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.
Learning Wasserstein Embeddings
Our goal is to alleviate this problem by providing an approximation mechanism that allows to break its inherent complexity.
Joint Distribution Optimal Transportation for Domain Adaptation
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.
Mapping Estimation for Discrete Optimal Transport
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.
Optimal spectral transportation with application to music transcription
Many spectral unmixing methods rely on the non-negative decomposition of spectral data onto a dictionary of spectral templates.
Wasserstein Discriminant Analysis
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
In this paper, we tackle the question of discovering an effective set of spatial filters to solve hyperspectral classification problems.
Generalized conditional gradient: analysis of convergence and applications
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
Domain adaptation from one data space (or domain) to another is one of the most challenging tasks of modern data analytics.
