| 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 | 446 | 2020 |
| 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 | 67 | 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 | 66 | 2020 |
| Domain Adaptation with Auxiliary Target Domain-Oriented Classifier J Liang, D Hu, J Feng IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021 | 58 | 2021 |
| 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 | 56 | 2021 |
| 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 | 24 | 2021 |
| 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 | 23* | 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 | 17* | 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 | 13* | 2022 |
| Umad: Universal model adaptation under domain and category shift J Liang, D Hu, J Feng, R He arXiv preprint arXiv:2112.08553, 2021 | 2 | 2021 |
