XingChao Peng
XingChao Peng
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TitleCited byYear
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
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
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
Visda: The visual domain adaptation challenge
X Peng, B Usman, N Kaushik, J Hoffman, D Wang, K Saenko
arXiv preprint arXiv:1710.06924, 2017
Synthetic to real adaptation with generative correlation alignment networks
X Peng, K Saenko
arXiv preprint arXiv:1701.05524, 2017
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
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
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
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
Moment Matching for Multi-Source Domain Adaptation
X Peng, Q Bai, X Xia, Z Huang, K Saenko, B Wang
arXiv preprint arXiv:1812.01754, 2018
Combining texture and shape cues for object recognition with minimal supervision
X Peng, K Saenko
Asian Conference on Computer Vision, 256-272, 2016
What Do Deep CNNs Learn About Objects?
X Peng, B Sun, K Ali, K Saenko
arXiv preprint arXiv:1504.02485, 2015
Domain Agnostic Learning with Disentangled Representations
X Peng, Z Huang, X Sun, K Saenko
arXiv preprint arXiv:1904.12347, 2019
Combining Texture and Shape Cues for Object Detection with Minimal Supervision
X Peng
submitted Sep 14, 2016
Adapting control policies from simulation to reality using a pairwise loss
U Viereck, X Peng, K Saenko, R Platt
arXiv preprint arXiv:1807.10413, 2018
Ground2sky label transfer for fine-grained aerial car recognition
B Sun, X Peng, XY Stella, K Saenko
2017 IEEE International Conference on Image Processing (ICIP), 360-364, 2017
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