We propose a framework that learns a representation transferable across different domains and tasks in a label efficient manner. Our approach battles domain shift with a domain adversarial loss, and generalizes the embedding to novel task using a metric learning-based approach. Our model is simultaneously optimized on labeled source data and unlabeled or sparsely labeled data in the target domain. Our method shows compelling results on novel classes within a new domain even when only a few labeled examples per class are available, outperforming the prevalent fine-tuning approach. In addition, we demonstrate the effectiveness of our framework on the transfer learning task from image object recognition to video action recognition.
In REF , Luo et al. design a framework that utilizes a domain adversarial loss to battle domain shift and then use a metric learning-based approach to apply the learned embedding to new tasks.
Label Efficient Learning of Transferable Representations across Domains and Tasks
ArXiv
ArXiv
Computer Science Mathematics