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              <p>Domain Adaptation methods seek to generalize the knowledge learned on a labeled source domain across another unlabeled target domain. Most of the deep learning methods for domain adaptation address the classification task, while regression models are still one step behind with some positive results in a shallow framework. Existing deep models for regression adaptation tasks rely on aligning the eigenvectors of both source and target data. This process, although providing satisfying results, is however unstable and not a backpropagation-friendly process. In this paper, we present a novel deep adaptation model based on aligning the non-negative sub-spaces derived from source and target domains. Removing the orthogonality constraints makes the model more stable for training. The proposed method is evaluated on a domain adaptation regression benchmark. Results show competitive performance compared to state-of-the-art models.</p>
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              <p>-Les approches d'adaptation de domaine cherchent à généraliser les connaissances acquises dans un domaine source étiqueté à un autre domaine cible non étiqueté. La plupart des approches d'apprentissage profond avec adaptation de domaine visent la tâche de classification. Parmi les modèles profonds existant pour les tâches de régression, l'adaptation de domaine peut être réalisée en alignant les vecteurs propres des données source et cible. Ce processus, bien qu'il donne des résultats acceptables, est cependant instable et n'est pas favorable à la rétropropagation du gradient. Dans cet article, nous présentons un nouveau modèle d'adaptation de domaine pour réseau de neurones profond basé sur l'alignement des sous-espaces non négatifs dérivés des domaines source et cible. L'élimination des contraintes d'orthogonalité rend le modèle plus stable en apprentissage. Notre approche est évaluée sur un jeu de données de référence pour l'adaptation de domaine en régression. Les résultats montrent des performances compétitives par rapport aux autres modèles de pointe.</p>
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