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                <term xml:lang="en">dynamic time warping</term>
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                <term xml:lang="en">time series</term>
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              <p>Large amounts of unlabeled data are often available, and the annotation step is usually a tedious and/or costly task. Unsupervised domain adaptation can address this issue by leveraging labels from a source domain to classify data from a related, yet different, target domain. When dealing with time series data, additional challenges arise due to potential temporal shifts alongside the feature distribution shift. We introduce the Match-And-Deform (MAD) approach to address these challenges. MAD aims at identifying matching between source and target time series while taking into accounttemporal distortions. To achieve this, the associated optimization problem simultaneously (1) aligns the series using an optimal transport loss and (2) adjusts the timestamps using dynamic time warping. When integrated into a deep neural network, MAD facilitates the learning of new representations of time series that align the domains and enhance the network’s discriminative power. Empirical evaluations conducted on benchmark datasets and real remote sensing data demonstrate MAD’s effectiveness. These numerical experiments show meaningful sample-to-sample matching and accurately estimates time shifts. Comparable or superior classification performance compared to state-of-the-art deep time series domain adaptation strategies are also achieved.This paper is adapted from Painblanc et al. [2023]. Code and data are publicly available: https://github.com/rtavenar/MatchAndDeform</p>
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              <p>De grandes quantités de données non étiquetées sont souvent disponibles, mais l'étape d’annotation est généralement une tâche fastidieuse et/ou coûteuse. En apprentissage non supervisé, l’adaptation de domaine peut résoudre ce problème en exploitant les étiquettes d’un domaine source pour classifier des données d’un domaine cible, similaire mais différent. Dans le cas de séries temporelles, des défis supplémentaires surviennent, notamment en raison des décalages temporels potentiels qui s’ajoutent aux décalages de distribution entre les domaines.Nous présentons une méthode dénommée Match-And-Deform (MAD) qui vise à relever ces défis en identifiant les correspondances entre les séries temporelles des domaines source et cible tout en tenant compte des distorsions temporelles. Le problème d’optimisation associé aligne simultanément (1) les séries en optimisant un coût de transport optimal et (2) les temps à l’aide de dynamic time warping. Intégré dans un réseau de neurones profond, MAD permet l’apprentissage de nouvelles représentations des séries temporelles, alignant les domaines et améliorant le pouvoir discriminant du réseau. La méthode est évaluée empiriquement sur des données de référence et des données réelles de télédétection, démontrant l’efficacité de MAD: les séries sont appariées de fa¸con pertinentes et les décalages temporels sont estimés avec précision. Des performances de classification comparables ou supérieures aux stratégies d’adaptation de domaine de séries temporelles profondes de  l'état de l’art sont également obtenues.Cet article est issu de Painblanc et al. [2023]. Le code et le jeu de données sont disponibles publiquement: https://github.com/rtavenar/MatchAndDeform</p>
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