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Label Efficient Learning of Transferable Representations acrosss Domains and Tasks
Zelun Luo · Yuliang Zou · Judy Hoffman · Li Fei-Fei

Mon Dec 06:30 PM -- 10:30 PM PST @ Pacific Ballroom #8 #None

We propose a framework that learns a representation transferable across different domains and tasks in a data 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.

Author Information

Alan Luo (Stanford University)
Yuliang Zou (Virginia Tech)
Judy Hoffman (FAIR and Georgia Tech)
Li Fei-Fei (Stanford University & Google)

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