Adversarial Learning for Zero-shot Domain Adaptation

• URL: http://arxiv.org/abs/2009.05214v1
• Date: Fri, 11 Sep 2020 03:41:32 GMT
• Title: Adversarial Learning for Zero-shot Domain Adaptation
• Authors: Jinghua Wang and Jianmin Jiang
• Abstract summary: Zero-shot domain adaptation is a problem where neither data sample nor label is available for parameter learning in the target domain. We propose a new method for ZSDA by transferring domain shift from an irrelevant task to the task of interest. We evaluate the proposed method on benchmark datasets and achieve the state-of-the-art performances.
• Score: 31.334196673143257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Zero-shot domain adaptation (ZSDA) is a category of domain adaptation problems where neither data sample nor label is available for parameter learning in the target domain. With the hypothesis that the shift between a given pair of domains is shared across tasks, we propose a new method for ZSDA by transferring domain shift from an irrelevant task (IrT) to the task of interest (ToI). Specifically, we first identify an IrT, where dual-domain samples are available, and capture the domain shift with a coupled generative adversarial networks (CoGAN) in this task. Then, we train a CoGAN for the ToI and restrict it to carry the same domain shift as the CoGAN for IrT does. In addition, we introduce a pair of co-training classifiers to regularize the training procedure of CoGAN in the ToI. The proposed method not only derives machine learning models for the non-available target-domain data, but also synthesizes the data themselves. We evaluate the proposed method on benchmark datasets and achieve the state-of-the-art performances.

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