Label Efficient Learning of Transferable Representations across Domains and Tasks [article]

Zelun Luo, Yuliang Zou, Judy Hoffman, Li Fei-Fei
2017 arXiv   pre-print
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
more » ... ed 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.
arXiv:1712.00123v1 fatcat:r6f2zfpwtbf7vewux4m7rrs4ai