
Unbalanced minibatch Optimal Transport; applications to Domain Adaptation
Optimal transport distances have found many applications in machine lear...
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Learning to Generate Wasserstein Barycenters
Optimal transport is a notoriously difficult problem to solve numericall...
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Online Graph Dictionary Learning
Dictionary learning is a key tool for representation learning, that expl...
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Minibatch optimal transport distances; analysis and applications
Optimal transport distances have become a classic tool to compare probab...
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Contextual Semantic Interpretability
Convolutional neural networks (CNN) are known to learn an image represen...
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Representation Transfer by Optimal Transport
Deep learning currently provides the best representations of complex obj...
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A Cycle GAN Approach for Heterogeneous Domain Adaptation in Land Use Classification
In the field of remote sensing and more specifically in Earth Observatio...
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Time Series Alignment with Global Invariances
In this work we address the problem of comparing time series while takin...
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COOptimal Transport
Optimal transport (OT) is a powerful geometric and probabilistic tool fo...
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Generating Natural Adversarial Hyperspectral examples with a modified Wasserstein GAN
Adversarial examples are a hot topic due to their abilities to fool a cl...
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Learning with minibatch Wasserstein : asymptotic and gradient properties
Optimal transport distances are powerful tools to compare probability di...
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Sliced GromovWasserstein
Recently used in various machine learning contexts, the GromovWasserste...
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Pushing the right boundaries matters! Wasserstein Adversarial Training for Label Noise
Noisy labels often occur in vision datasets, especially when they are is...
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Endtoend Learning for Early Classification of Time Series
Classification of time series is a topical issue in machine learning. Wh...
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Fused GromovWasserstein distance for structured objects: theoretical foundations and mathematical properties
Optimal transport theory has recently found many applications in machine...
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An Entropic Optimal Transport Loss for Learning Deep Neural Networks under Label Noise in Remote Sensing Images
Deep neural networks have established as a powerful tool for large scale...
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Optimal Transport for structured data
Optimal transport has recently gained a lot of interest in the machine l...
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DeepJDOT: Deep Joint distribution optimal transport for unsupervised domain adaptation
In computer vision, one is often confronted with problems of domain shif...
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Optimal Transport for Multisource Domain Adaptation under Target Shift
In this paper, we propose to tackle the problem of reducing discrepancie...
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Wasserstein Distance Measure Machines
This paper presents a distancebased discriminative framework for learni...
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Data Dependent Kernel Approximation using Pseudo Random Fourier Features
Kernel methods are powerful and flexible approach to solve many problems...
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LargeScale Optimal Transport and Mapping Estimation
This paper presents a novel twostep approach for the fundamental proble...
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Learning Wasserstein Embeddings
The Wasserstein distance received a lot of attention recently in the com...
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Joint Distribution Optimal Transportation for Domain Adaptation
This paper deals with the unsupervised domain adaptation problem, where ...
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Optimal spectral transportation with application to music transcription
Many spectral unmixing methods rely on the nonnegative decomposition of...
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Wasserstein Discriminant Analysis
Wasserstein Discriminant Analysis (WDA) is a new supervised method that ...
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Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions
In this paper, we tackle the question of discovering an effective set of...
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Generalized conditional gradient: analysis of convergence and applications
The objectives of this technical report is to provide additional results...
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