XingChao Peng
XingChao Peng
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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
Visda: The visual domain adaptation challenge
X Peng, B Usman, N Kaushik, J Hoffman, D Wang, K Saenko
arXiv preprint arXiv:1710.06924, 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
Federated adversarial domain adaptation
X Peng, Z Huang, Y Zhu, K Saenko
arXiv preprint arXiv:1911.02054, 2019
Domain agnostic learning with disentangled representations
X Peng, Z Huang, X Sun, K Saenko
International conference on machine learning, 5102-5112, 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
Synthetic to real adaptation with generative correlation alignment networks
X Peng, K Saenko
arXiv preprint arXiv:1701.05524, 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
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
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
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
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
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
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
Network architecture search for domain adaptation
Y Li, X Peng
arXiv preprint arXiv:2008.05706, 2020
Generalized domain adaptation with covariate and label shift co-alignment
S Tan, X Peng, K Saenko
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
Learning domain adaptive features with unlabeled domain bridges
Y Li, X Peng
arXiv preprint arXiv:1912.05004, 2019
What Do Deep CNNs Learn About Objects?
X Peng, B Sun, K Ali, K Saenko
arXiv preprint arXiv:1504.02485, 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
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