2 citations · 2 across the 5 of their papers we have counts for
6 papers
A Comprehensive Benchmark of Source-Free Universal Domain Adaptation on Time Series Representations
Romain Mussard, Fannia Pacheco, Maxime Berar +2
Source-Free Universal Domain Adaptation (SF-UniDA) extends Universal Domain Adaptation by removing access to source data at adaptation time while still handling label-set mismatche…
Two-sample test with Wasserstein distance on Gaussian samples based on a log-normal approximation
Johann Clément-Cottuz, Maxime Bérar, Gilles Gasso
In this article, we propose a two-sample test based on the Wasserstein 2-distance between two 1D samples, where the first sample is assumed to be Gaussian based on an approximation…
Spectral-Spatial Contrastive Learning Framework for Regression on Hyperspectral Data
Mohamad Dhaini, Paul Honeine, Maxime Berar +1
Contrastive learning has demonstrated great success in representation learning, especially for image classification tasks. However, there is still a shortage in studies targeting r…
Universal Domain Adaptation Benchmark for Time Series Data Representation
Romain Mussard, Fannia Pacheco, Maxime Berar +2
Deep learning models have significantly improved the ability to detect novelties in time series (TS) data. This success is attributed to their strong representation capabilities. H…
Deep Joint Distribution Optimal Transport for Universal Domain Adaptation on Time Series
Romain Mussard, Fannia Pacheco, Maxime Berar +2
Universal Domain Adaptation (UniDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain, even when their classes are not fully shared. Few dedicat…
Contrastive Learning for Regression on Hyperspectral Data
Mohamad Dhaini, Maxime Berar, Paul Honeine +1
Contrastive learning has demonstrated great effectiveness in representation learning especially for image classification tasks. However, there is still a shortage in the studies ta…