45 citations · 93 across the 6 of their papers we have counts for
7 papers
SIFTER: A Task-specific Alignment Strategy for Enhancing Sentence Embeddings
Chao Yu, Wenhao Zhu, Chaoming Liu +2
The paradigm of pre-training followed by fine-tuning on downstream tasks has become the mainstream method in natural language processing tasks. Although pre-trained models have the…
Learning Invariant Representations across Domains and Tasks
Jindong Wang, Wenjie Feng, Chang Liu +5
Being expensive and time-consuming to collect massive COVID-19 image samples to train deep classification models, transfer learning is a promising approach by transferring knowledg…
Object Detection Made Simpler by Eliminating Heuristic NMS
Qiang Zhou, Chaohui Yu, Chunhua Shen +2
We show a simple NMS-free, end-to-end object detection framework, of which the network is a minimal modification to a one-stage object detector such as the FCOS detection model [Ti…
Learning to Match Distributions for Domain Adaptation
Chaohui Yu, Jindong Wang, Chang Liu +5
When the training and test data are from different distributions, domain adaptation is needed to reduce dataset bias to improve the model's generalization ability. Since it is diff…
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…
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…