Related papers: Fisher Deep Domain Adaptation

Fisher Deep Domain Adaptation

URL: http://arxiv.org/abs/2003.05636v1
Date: Thu, 12 Mar 2020 06:17:48 GMT
Title: Fisher Deep Domain Adaptation
Authors: Yinghua Zhang, Yu Zhang, Ying Wei, Kun Bai, Yangqiu Song, Qiang Yang
Abstract summary: Deep domain adaptation models learn a neural network in an unlabeled target domain by leveraging the knowledge from a labeled source domain. A Fisher loss is proposed to learn discriminative representations which are within-class compact and between-class separable.
Score: 41.50519723389471
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Deep domain adaptation models learn a neural network in an unlabeled target domain by leveraging the knowledge from a labeled source domain. This can be achieved by learning a domain-invariant feature space. Though the learned representations are separable in the source domain, they usually have a large variance and samples with different class labels tend to overlap in the target domain, which yields suboptimal adaptation performance. To fill the gap, a Fisher loss is proposed to learn discriminative representations which are within-class compact and between-class separable. Experimental results on two benchmark datasets show that the Fisher loss is a general and effective loss for deep domain adaptation. Noticeable improvements are brought when it is used together with widely adopted transfer criteria, including MMD, CORAL and domain adversarial loss. For example, an absolute improvement of 6.67% in terms of the mean accuracy is attained when the Fisher loss is used together with the domain adversarial loss on the Office-Home dataset.

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