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We use information-theoretic tools to derive a novel analysis of Multi-sourceDomain Adaptation (MDA) from the representation learning perspective.Concretely, we study joint distribution alignment for supervised MDA with fewtarget labels and unsupervised MDA with pseudo labels, where the latter isrelatively hard and less commonly studied. We further providealgorithm-dependent generalization bounds for these two settings, where thegeneralization is characterized by the mutual information between theparameters and the data. Then we propose a novel deep MDA algorithm, implicitlyaddressing the target shift through joint alignment. Finally, the mutualinformation bounds are extended to this algorithm providing a non-vacuousgradient-norm estimation. The proposed algorithm has comparable performance tothe state-of-the-art on target-shifted MDA benchmark with improved memoryefficiency.

Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation