| Moment matching for multi-source domain adaptation X Peng, Q Bai, X Xia, Z Huang, K Saenko, B Wang Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 1944 | 2019 |
| Visda: The visual domain adaptation challenge X Peng, B Usman, N Kaushik, J Hoffman, D Wang, K Saenko arXiv preprint arXiv:1710.06924, 2017 | 858 | 2017 |
| Learning deep object detectors from 3d models X Peng, B Sun, K Ali, K Saenko Proceedings of the IEEE International Conference on Computer Vision, 1278-1286, 2015 | 459 | 2015 |
| Federated adversarial domain adaptation X Peng, Z Huang, Y Zhu, K Saenko arXiv preprint arXiv:1911.02054, 2019 | 314 | 2019 |
| Domain agnostic learning with disentangled representations X Peng, Z Huang, X Sun, K Saenko International conference on machine learning, 5102-5112, 2019 | 293 | 2019 |
| Visda: A synthetic-to-real benchmark for visual domain adaptation X Peng, B Usman, N Kaushik, D Wang, J Hoffman, K Saenko Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 207 | 2018 |
| Synthetic to real adaptation with generative correlation alignment networks X Peng, K Saenko arXiv preprint arXiv:1701.05524, 2017 | 137 | 2017 |
| Towards adapting deep visuomotor representations from simulated to real environments E Tzeng, C Devin, J Hoffman, C Finn, X Peng, S Levine, K Saenko, ... arXiv preprint arXiv:1511.07111 2 (3), 2015 | 121 | 2015 |
| Syn2real: A new benchmark forsynthetic-to-real visual domain adaptation X Peng, B Usman, K Saito, N Kaushik, J Hoffman, K Saenko arXiv preprint arXiv:1806.09755, 2018 | 98 | 2018 |
| Class-imbalanced domain adaptation: An empirical odyssey S Tan, X Peng, K Saenko Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020 …, 2020 | 97 | 2020 |
| Fine-to-coarse Knowledge Transfer For Low-Res Image Classification X Peng, J Hoffman, SX Yu, K Saenko IEEE International Conference on Image Processing 2016, 2016 | 82 | 2016 |
| Exploring invariances in deep convolutional neural networks using synthetic images X Peng, B Sun, K Ali, K Saenko CoRR, abs/1412.7122 2 (4), 2014 | 66 | 2014 |
| Domain2vec: Domain embedding for unsupervised domain adaptation X Peng, Y Li, K Saenko Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 37 | 2020 |
| Generating large scale image datasets from 3D CAD models B Sun, X Peng, K Saenko CVPR 2015 Workshop on The Future of Datasets in Vision, 2015 | 15 | 2015 |
| Network architecture search for domain adaptation Y Li, X Peng arXiv preprint arXiv:2008.05706, 2020 | 14 | 2020 |
| Generalized domain adaptation with covariate and label shift co-alignment S Tan, X Peng, K Saenko | 12 | 2019 |
| Combining texture and shape cues for object recognition with minimal supervision X Peng, K Saenko Computer Vision–ACCV 2016: 13th Asian Conference on Computer Vision, Taipei …, 2017 | 6 | 2017 |
| Learning domain adaptive features with unlabeled domain bridges Y Li, X Peng arXiv preprint arXiv:1912.05004, 2019 | 4 | 2019 |
| What Do Deep CNNs Learn About Objects? X Peng, B Sun, K Ali, K Saenko arXiv preprint arXiv:1504.02485, 2015 | 4 | 2015 |
| Adapting control policies from simulation to reality using a pairwise loss U Viereck, X Peng, K Saenko, R Platt Proceedings of the 2018 International Symposium on Experimental Robotics …, 2020 | 2 | 2020 |
Kate SaenkoBoston UniversityZweryfikowany adres z bu.edu
