Domain adaptation is a powerful technique given a wide amount of labeled data from similar attributes in different domains. In real-world applications, there is a huge number of data but almost more of them are unlabeled. It is effective in image classification where it is expensive and time-consuming to obtain adequate label data. We propose a novel method named DALRRL, which consists of deep ...

transfer learning allows the knowledge transference from the source (training dataset) to target (test dataset) domain. feature selection for transfer learning (f-mmd) is a simple and effective transfer learning method, which tackles the domain shift problem. f-mmd has good performance on small-sized datasets, but it suffers from two major issues: i) computational efficiency and predictive perf...

The development of deep learning techniques has allowed Neural Machine Translation (NMT) models to become extremely powerful, given sufficient training data and time. However, systems struggle when translating text from a new domain with distinct style or vocabulary. Fine-tuning on in-domain allows good adaptation, but requires relevant bilingual data. Even if this is available, simple fine-tun...