Abstract: In this paper, we addressed the limitation of relying solely on distribution alignment and source-domain empirical risk minimization in Unsupervised Domain Adaptation (UDA). Our ...
For years, the guiding assumption of artificial intelligence has been simple: an AI is only as good as the data it has seen.
Abstract: There is a vast literature on representation learning based on principles such as coding efficiency, statistical independence, causality, controllability, or symmetry. In this paper we ...
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