385 citations · 1.1k across the 45 of their papers we have counts for
3 papers · 1 filter
Learning with minibatch Wasserstein : asymptotic and gradient properties
Kilian Fatras, Younes Zine, Rémi Flamary +2
Optimal transport distances are powerful tools to compare probability distributions and have found many applications in machine learning. Yet their algorithmic complexity prevents…
Wasserstein Adversarial Regularization (WAR) on label noise
Kilian Fatras, Bharath Bhushan Damodaran, Sylvain Lobry +3
Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning ro…
End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping
Marc Rußwurm, Nicolas Courty, Rémi Emonet +3
Remote sensing satellites capture the cyclic dynamics of our Planet in regular time intervals recorded in satellite time series data. End-to-end trained deep learning models use th…