| 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 | 1420 | 2020 |
| 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 | 343 | 2021 |
| 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 | 290 | 2021 |
| Domain Adaptation with Auxiliary Target Domain-Oriented Classifier J Liang, D Hu, J Feng IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021 | 215 | 2021 |
| 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 | 150 | 2020 |
| 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 | 126* | 2022 |
| 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 | 71 | 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 | 49* | 2022 |
| 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 | 45* | 2021 |
| UMAD: Universal Model Adaptation under Domain and Category Shift J Liang, D Hu, J Feng, R He arXiv preprint arXiv:2112.08553, 2021 | 32 | 2021 |
| Mixed Samples as Probes for Unsupervised Model Selection in Domain Adaptation D Hu, J Liang, JH Liew, C Xue, S Bai, X Wang Advances in Neural Information Processing Systems (NeurIPS), 2023 | 6 | 2023 |
| Pseudo-Calibration: Improving Predictive Uncertainty Estimation in Unsupervised Domain Adaptation D Hu, J Liang, X Wang, CS Foo International Conference on Machine Learning (ICML), 2024 | 1* | 2024 |
| Validation of Unsupervised Adaptive Models S Bai, D Hu, JH Liew, C Xue US Patent App. 18/070,318, 2024 | | 2024 |
| Towards Reliable Model Selection for Unsupervised Domain Adaptation: An Empirical Study and A Certified Baseline D Hu, M Luo, J Liang, CS Foo Advances in Neural Information Processing Systems (NeurIPS) Track Datasets …, 2024 | | 2024 |
