Adversarial Learning for Zero-shot Domain Adaptation
This addresses domain adaptation for scenarios where target data is unavailable, though it appears incremental as it builds on existing GAN-based methods.
The paper tackles zero-shot domain adaptation, where no target domain data or labels are available, by transferring domain shift from an irrelevant task to the task of interest using coupled generative adversarial networks and co-training classifiers, achieving state-of-the-art performance on benchmark datasets.
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.