Training a model to perform a task typically requires a large amount of data from the domains in which the task will be applied. However, it is often the case that data are abundant in some domains but scarce in others. Domain adaptation deals with the challenge of adapting a model trained from a data-rich source domain to perform well in a data-poor target domain. In general, this requires learning plausible mappings between domains. CycleGAN is a powerful framework that efficiently learns to map inputs from one domain to another using adversarial training and a cycle-consistency constraint. However, the conventional approach of enforcing cycle-consistency via reconstruction may be overly restrictive in cases where one or more domains have limited training data. In this paper, we propose an augmented cyclic adversarial learning model that enforces the cycle-consistency constraint through an external task specific model, which encourages the preservation of task-relevant \textit{content} as opposed to exact reconstruction. This task specific model both relaxes the cycle-consistency constraint and complements the role of the discriminator during training, serving as an augmented information source for learning the mapping. In the experiment, we adopt a speech recognition model from each domain as the task specific model. Our approach improves absolute performance of speech recognition by 2\% for female speakers in the TIMIT dataset, where the majority of training samples are from male voices. We also explore digit classification with MNIST and SVHN in a low-resource setting and show that our approach improves absolute performance by 14\% and 4\% when adapting SVHN to MNIST and vice versa, respectively. Our approach also outperforms unsupervised domain adaptation methods, which require high-resource unlabeled target domain.

本文介绍了一种增强循环对抗学习模型，用于域适应并解决在其中数据丰富的域中对任务的学习问题，通过迁移学习及结合声音识别任务的结果，在低资源环境下将 SVHN 和 MNIST 彼此转换的分类准确率分别提升了14%和4%。

增强循环对抗学习用于低资源域自适应