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- W4387185507 abstract "This paper seeks to solve Multi-Source Domain Adaptation (MSDA), which aims to mitigate data distribution shifts when transferring knowledge from multiple labeled source domains to an unlabeled target domain. We propose a novel MSDA framework based on dictionary learning and optimal transport. We interpret each domain in MSDA as an empirical distribution. As such, we express each domain as a Wasserstein barycenter of dictionary atoms, which are empirical distributions. We propose a novel algorithm, DaDiL, for learning via mini-batches: (i) atom distributions; (ii) a matrix of barycentric coordinates. Based on our dictionary, we propose two novel methods for MSDA: DaDil-R, based on the reconstruction of labeled samples in the target domain, and DaDiL-E, based on the ensembling of classifiers learned on atom distributions. We evaluate our methods in 3 benchmarks: Caltech-Office, Office 31, and CRWU, where we improved previous state-of-the-art by 3.15%, 2.29%, and 7.71% in classification performance. Finally, we show that interpolations in the Wasserstein hull of learned atoms provide data that can generalize to the target domain." @default.
- W4387185507 created "2023-09-30" @default.
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- W4387185507 date "2023-09-28" @default.
- W4387185507 modified "2023-10-18" @default.
- W4387185507 title "Multi-Source Domain Adaptation Through Dataset Dictionary Learning in Wasserstein Space" @default.
- W4387185507 doi "https://doi.org/10.3233/faia230459" @default.
- W4387185507 hasPublicationYear "2023" @default.
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