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Transfer Learning with Dynamic Adversarial Adaptation Network
Chaohui Yu, Jindong Wang, Yiqiang Chen +1
The recent advances in deep transfer learning reveal that adversarial learning can be embedded into deep networks to learn more transferable features to reduce the distribution dis…
Transfer Learning with Dynamic Distribution Adaptation
Jindong Wang, Yiqiang Chen, Wenjie Feng +3
Transfer learning aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Since the source and the target domains are usually from diff…
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
Yiqiang Chen, Jindong Wang, Chaohui Yu +2
With the rapid development of computing technology, wearable devices such as smart phones and wristbands make it easy to get access to people's health information including activit…
Easy Transfer Learning By Exploiting Intra-domain Structures
Jindong Wang, Yiqiang Chen, Han Yu +2
Transfer learning aims at transferring knowledge from a well-labeled domain to a similar but different domain with limited or no labels. Unfortunately, existing learning-based meth…
Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning
Chaohui Yu, Jindong Wang, Yiqiang Chen +1
Deep unsupervised domain adaptation (UDA) has recently received increasing attention from researchers. However, existing methods are computationally intensive due to the computatio…