385 citations · 1.5k across the 64 of their papers we have counts for
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Contextual Semantic Interpretability
Diego Marcos, Ruth Fong, Sylvain Lobry +3
Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpre…
An Entropic Optimal Transport Loss for Learning Deep Neural Networks under Label Noise in Remote Sensing Images
Bharath Bhushan Damodaran, Rémi Flamary, Viven Seguy +1
Deep neural networks have established as a powerful tool for large scale supervised classification tasks. The state-of-the-art performances of deep neural networks are conditioned…
DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation
Bharath Bhushan Damodaran, Benjamin Kellenberger, Rémi Flamary +2
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 cha…
Multi-frequency image reconstruction for radio-interferometry with self-tuned regularization parameters
Rita Ammanouil, André Ferrari, Rémi Flamary +2
As the world's largest radio telescope, the Square Kilometer Array (SKA) will provide radio interferometric data with unprecedented detail. Image reconstruction algorithms for radi…
Astronomical image reconstruction with convolutional neural networks
Rémi Flamary
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 computational…