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Unsupervised domain adaptation: A multi-task learning-based method

机译：无监督域自适应：一种基于多任务学习的方法

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摘要

This paper presents a new perspective to formulate unsupervised domain adaptation as a multi-task learning problem. This formulation removes the commonly used assumption in the classifier-based adaptation approach that a shared classifier exists for the same task in different domains. Specifically, the source task is to learn a linear classifier from the labelled source data and the target task is to learn a linear transform to cluster the unlabelled target data such that the original target data are mapped to a lower dimensional subspace where the geometric structure is preserved. The two tasks are jointly learned by enforcing the target transformation is close to the source classifier and the class distribution shift between domains is reduced in the meantime. Two novel classifier-based adaptation algorithms are proposed upon the formulation using Regularized Least Squares and Support Vector Machines respectively, in which unshared classifiers between the source and target domains are assumed and jointly learned to effectively deal with large domain shift. Experiments on both synthetic and real-world cross domain recognition tasks have shown that the proposed methods outperform several state-of-the-art unsupervised domain adaptation methods. (C) 2019 Elsevier B.V. All rights reserved.
机译：本文提出了一种新的观点，将无监督域自适应公式化为一个多任务学习问题。这种表述消除了在基于分类器的适应方法中常用的假设，即在不同领域中存在针对同一任务的共享分类器。具体来说，源任务是从标记的源数据中学习线性分类器，目标任务是学习线性变换以对未标记的目标数据进行聚类，以便将原始目标数据映射到几何结构为的较低维子空间保留。通过强制目标转换接近源分类器来共同学习这两个任务，同时减少了域之间的类分配偏移。分别基于正则化最小二乘和支持向量机，提出了两种基于分类器的新型自适应算法，其中假设了源域和目标域之间的非共享分类器，并共同学习以有效地处理大域偏移。对合成和现实世界中跨域识别任务的实验表明，所提出的方法优于几种最新的无监督域自适应方法。 （C）2019 Elsevier B.V.保留所有权利。

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