Oct 3, 2022 · This paper uses information-theoretic tools to analyze the generalization error in unsupervised domain adaptation (UDA).
This paper uses information-theoretic tools to analyze the generalization error in unsupervised domain adaptation (UDA). We present novel upper bounds for ...
This paper uses information-theoretic tools to analyze the generalization error in unsupervised domain adaptation (UDA). We present novel upper bounds for ...
This paper uses information-theoretic tools to analyze the generalization error in unsupervised domain adaptation (UDA). We present novel upper bounds for ...
The aim of the domain-adaptation task for speaker verification is to exploit unlabelled target domain data by using the labelled source domain data ...
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Jun 27, 2012 · We study the problem of unsupervised domain adaptation, which aims to adapt classifiers trained on a labeled source domain to an unlabeled target domain.
We study the problem of unsupervised do- main adaptation, which aims to adapt classi- fiers trained on a labeled source domain to an unlabeled target domain.
We study the problem of unsupervised domain adaptation, which aims to adapt classifiers trained on a labeled source domain to an unlabeled target domain.
ABSTRACT. The aim of the domain-adaptation task for speaker verification is to exploit unlabelled target domain data by using the labelled source.
In this paper we explore the fundamental problems that may arise in unsupervised domain adaptation, and discuss conditions that might still make it work.