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Dapeng Hu
Dapeng Hu
NUS << NJU
Verified email at u.nus.edu
Title
Cited by
Cited by
Year
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
J Liang, D Hu, J Feng
International Conference on Machine Learning (ICML), 2020
2512020
A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation
J Liang, Y Wang, D Hu, R He, J Feng
European Conference on Computer Vision (ECCV), 2020
412020
Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer
J Liang, D Hu, Y Wang, R He, J Feng
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
302021
Domain Adaptation with Auxiliary Target Domain-Oriented Classifier
J Liang, D Hu, J Feng
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021
282021
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data
M Luo, F Chen, D Hu, Y Zhang, J Liang, J Feng
Advances in Neural Information Processing Systems (NeurIPS), 2021
162021
Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning
Y Zhang, B Hooi, D Hu, J Liang, J Feng
Advances in Neural Information Processing Systems (NeurIPS), 2021
142021
Adversarial Domain Adaptation with Prototype-Based Normalized Output Conditioner
D Hu, J Liang, Q Hou, H Yan, Y Chen
IEEE Transactions on Image Processing (TIP), 2021
13*2021
How Well Does Self-Supervised Pre-Training Perform with Streaming Data?
D Hu, S Yan, Q Lu, L Hong, H Hu, Y Zhang, Z Li, X Wang, J Feng
International Conference on Learning Representations (ICLR), 2022
10*2022
DINE: Domain Adaptation from Single and Multiple Black-box Predictors
J Liang, D Hu, J Feng, R He
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022
8*2022
UMAD: Universal Model Adaptation under Domain and Category Shift
J Liang, D Hu, J Feng, R He
arXiv preprint arXiv:2112.08553, 2021
2021
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