| 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, 6028-6039, 2020 | 595 | 2020 |
| Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-free Approach L He, J Liang, H Li, Z Sun Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 264 | 2018 |
| 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 44 (11), 8602 …, 2022 | 103 | 2022 |
| Aggregating Randomized Clustering-Promoting Invariant Projections for Domain Adaptation J Liang, R He, Z Sun, T Tan IEEE Transactions on Pattern Analysis and Machine Intelligence 41 (5), 1027-1042, 2019 | 97 | 2019 |
| Exploring Uncertainty in Pseudo-label Guided Unsupervised Domain Adaptation J Liang, R He, Z Sun, T Tan Pattern Recognition Journal, 2019 | 89 | 2019 |
| 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 Annual Conference on Neural Information Processing Systems, 5972-5984, 2021 | 87 | 2021 |
| Domain Adaptation with Auxiliary Target Domain-Oriented Classifier J Liang, D Hu, J Feng Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 82 | 2021 |
| Distant Supervised Centroid Shift: A Simple and Efficient Approach to Visual Domain Adaptation J Liang, R He, Z Sun, T Tan IEEE Conference on Computer Vision and Pattern Recognition, 2975-2984, 2019 | 79 | 2019 |
| 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, 123-140, 2020 | 77 | 2020 |
| DINE: Domain Adaptation from Single and Multiple Black-box Predictors J Liang, D Hu, J Feng, R He Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 41* | 2022 |
| Self-Paced Learning: an Implicit Regularization Perspective Y Fan, R He, J Liang, BG Hu AAAI Conference on Artificial Intelligence, 1877-1883, 2017 | 39 | 2017 |
| Self-Paced Cross-Modal Subspace Matching J Liang, Z Li, D Cao, R He, J Wang International ACM SIGIR conference on Research and Development in …, 2016 | 36 | 2016 |
| Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning Y Zhang, B Hooi, D Hu, J Liang, J Feng Annual Conference on Neural Information Processing Systems, 29848-29860, 2021 | 32 | 2021 |
| Diagnostic Classification for Human Autism and Obsessive-Compulsive Disorder Based on Machine Learning From a Primate Genetic Model Y Zhan, J Wei, J Liang, X Xu, R He, TW Robbins, Z Wang American Journal of Psychiatry 178 (1), 65-76, 2021 | 32 | 2021 |
| Adversarial Domain Adaptation with Prototype-Based Normalized Output Conditioner D Hu, J Liang, Q Hou, H Yan, Y Chen IEEE Transactions on Image Processing 30, 9359-9371, 2021 | 25* | 2021 |
| Deep Semantic Reconstruction Hashing for Similarity Retrieval Y Wang, X Ou, J Liang, Z Sun IEEE Transactions on Circuits and Systems for Video Technology 31 (1), 387-400, 2021 | 23 | 2021 |
| Group-Invariant Cross-Modal Subspace Learning J Liang, R He, Z Sun, T Tan International Joint Conference on Artificial Intelligence, 1739-1745, 2016 | 21 | 2016 |
| Learning Discriminative Geodesic Flow Kernel for Unsupervised Domain Adaptation J Wei, J Liang, R He, J Yang IEEE International Conference on Multimedia and Expo, 1-6, 2018 | 17 | 2018 |
| Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning Y Shi, K Zhou, J Liang, Z Jiang, J Feng, P Torr, S Bai, VYF Tan Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 16 | 2022 |
| Robust Localized Multi-view Subspace Clustering Y Fan, J Liang, R He, BG Hu, S Lyu arXiv preprint arXiv:1705.07777, 2017 | 13 | 2017 |
Ran He, IAPR FellowNational Laboratory of Pattern Recognition, CASIAVerified email at nlpr.ia.ac.cn
