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In this paper, we consider the intersection of two problems in machinelearning: Multi-Source Domain Adaptation (MSDA) and Dataset Distillation (DD).On the one hand, the first considers adapting multiple heterogeneous labeledsource domains to an unlabeled target domain. On the other hand, the secondattacks the problem of synthesizing a small summary containing all theinformation about the datasets. We thus consider a new problem called MSDA-DD.To solve it, we adapt previous works in the MSDA literature, such asWasserstein Barycenter Transport and Dataset Dictionary Learning, as well as DDmethod Distribution Matching. We thoroughly experiment with this novel problemon four benchmarks (Caltech-Office 10, Tennessee-Eastman Process, ContinuousStirred Tank Reactor, and Case Western Reserve University), where we show that,even with as little as 1 sample per class, one achieves state-of-the-artadaptation performance.

Multi-Source Domain Adaptation meets Dataset Distillation through Dataset Dictionary Learning