Zobrazeno 1 - 10
of 112
pro vyhledávání: '"Gasso, Gilles"'
Gaussian smoothed sliced Wasserstein distance has been recently introduced for comparing probability distributions, while preserving privacy on the data. It has been shown that it provides performances similar to its non-smoothed (non-private) counte
Externí odkaz:
http://arxiv.org/abs/2404.03273
Adversarial learning baselines for domain adaptation (DA) approaches in the context of semantic segmentation are under explored in semi-supervised framework. These baselines involve solely the available labeled target samples in the supervision loss.
Externí odkaz:
http://arxiv.org/abs/2312.07370
Wasserstein distance (WD) and the associated optimal transport plan have been proven useful in many applications where probability measures are at stake. In this paper, we propose a new proxy of the squared WD, coined min-SWGG, that is based on the t
Externí odkaz:
http://arxiv.org/abs/2307.01770
Autor:
Ruffino, Cyprien, Blin, Rachel, Ainouz, Samia, Gasso, Gilles, Hérault, Romain, Meriaudeau, Fabrice, Canu, Stéphane
Polarimetric imaging, along with deep learning, has shown improved performances on different tasks including scene analysis. However, its robustness may be questioned because of the small size of the training datasets. Though the issue could be solve
Externí odkaz:
http://arxiv.org/abs/2206.07431
Autor:
Trabelsi, Imen, Hérault, Romain, Baillet, Héloise, Thouvarecq, Régis, Seifert, Ludovic, Gasso, Gilles
Publikováno v:
In Biomedical Signal Processing and Control January 2025 99
Gaussian smoothed sliced Wasserstein distance has been recently introduced for comparing probability distributions, while preserving privacy on the data. It has been shown, in applications such as domain adaptation, to provide performances similar to
Externí odkaz:
http://arxiv.org/abs/2110.10524
This paper addresses the problem of Unbalanced Optimal Transport (UOT) in which the marginal conditions are relaxed (using weighted penalties in lieu of equality) and no additional regularization is enforced on the OT plan. In this context, we show t
Externí odkaz:
http://arxiv.org/abs/2106.04145
Optimal Transport (OT) metrics allow for defining discrepancies between two probability measures. Wasserstein distance is for longer the celebrated OT-distance frequently-used in the literature, which seeks probability distributions to be supported o
Externí odkaz:
http://arxiv.org/abs/2106.02542
Common compartmental modeling for COVID-19 is based on a priori knowledge and numerous assumptions. Additionally, they do not systematically incorporate asymptomatic cases. Our study aimed at providing a framework for data-driven approaches, by lever
Externí odkaz:
http://arxiv.org/abs/2106.11918
We present a 2-step optimal transport approach that performs a mapping from a source distribution to a target distribution. Here, the target has the particularity to present new classes not present in the source domain. The first step of the approach
Externí odkaz:
http://arxiv.org/abs/2010.01045