Heterogeneous Domain Adaptation & HFA

Bicelove 2014-06-27 09:24:04

有人在看Learning with Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation这篇论文吗？这是2014年IEEE的一篇文章，作者是Wen Li,Student Member, IEEE, Lixin Duan, Dong Xu,Senior Member, IEEE, and Ivor W. Tsang。探讨下

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Domain adaptation is an active, emerging research area that attempts to address the changes in data distribution across training and testing datasets. With the availability of a multitude of image acquisition sen- sors, variations due to illumination, and viewpoint among others, com- puter vision applications present a very natural test bed for evaluating domain adaptation methods. In this monograph, we provide a compre- hensive overview of domain adaptation solutions for visual recognition problems. By starting with the problem description and illustrations, we discuss three adaptation scenarios namely, (i) unsupervised adap- tation where the “source domain” training data is partially labeled and the “target domain” test data is unlabeled, (ii) semi-supervised adaptation where the target domain also has partial labels, and (iii) multi-domain heterogeneous adaptation which studies the previous two settings with the source and/or target having more than one domain, and accounts for cases where the features used to represent the data in each domain are different. For all these topics we discuss existing adaptation techniques in the literature, which are motivated by the principles of max-margin discriminative learning, manifold learning, sparse coding, as well as low-rank representations. These techniques have shown improved performance on a variety of applications such as object recognition, face recognition, activity analysis, concept clas- sification, and person detection. We then conclude by analyzing the challenges posed by the realm of “big visual data”, in terms of the generalization ability of adaptation algorithms to unconstrained data acquisition as well as issues related to their computational tractability, and draw parallels with the efforts from vision community on image transformation models, and invariant descriptors so as to facilitate im- proved understanding of vision problems under uncertainty.

Heterogeneous Domain Adaptation 异质域适应论文合集 [ACM MM 2020] Simultaneous Semantic Alignment Network for Heterogeneous Domain Adaptation [paper] [github] [ACM MM 2019] Heterogeneous Domain Adaptation via Soft Transfer Network [paper] [2020] Mul...

Multi-source Heterogeneous Domain Adaptation with Conditional Weighting Adversarial Network [paper] 目录 Multi-source Heterogeneous Domain Adaptation with Conditional Weighting Adversarial Network Abstract Problem Formulation Conditional Weighting

introduction 作者认为，domain adaption（域适应）方法旨在通过学习domain-invariant feature（域不变特征）来桥接source domain和target domain，从而能够在target domain没有标签的情况下，利用source domain所学到的分类器对target domain进行预测。现在已经可以将domain adaption嵌入

Domain Adaptation 现有深度学习模型都不具有普适性，即在某个数据集上训练的结果只能在某个领域中有效，而很难迁移到其他的场景中，因此出现了迁移学习这一领域。其目标就是将原数据域（源域，source domain）尽可能好的迁移到目标域（target domain），Domain Adaptation任务中往往源域和目标域属于同一类任务，即源于为训练样本域（有标签），目标域为测集域，其测试集域无标签或只有少量标签，但是分布不同或数据差异大，具体根据这两点可以划分为： homogeneous .

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