Xingchao Peng

According to our database¹, Xingchao Peng authored at least 23 papers between 2014 and 2021.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

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In proceedings

Article

PhD thesis

Dataset

Other

Links

On csauthors.net:

Bibliography

2021

Weakly Supervised Domain Adaptation using Super-pixel labeling for Semantic Segmentation.

[BibT_eX]

[DOI]

Proceedings of the 17th International Conference on Machine Vision and Applications, 2021

2020

Domain adaptive learning with disentangled features

[BibT_eX]

[DOI]

Xingchao Peng

PhD thesis, 2020

Network Architecture Search for Domain Adaptation.

[BibT_eX]

[DOI]

Yichen Li

Xingchao Peng

CoRR, 2020

Federated Adversarial Domain Adaptation.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

Class-Imbalanced Domain Adaptation: An Empirical Odyssey.

[BibT_eX]

[DOI]

Shuhan Tan

Xingchao Peng

Kate Saenko

Proceedings of the Computer Vision - ECCV 2020 Workshops, 2020

Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation.

[BibT_eX]

[DOI]

Xingchao Peng

Yichen Li

Kate Saenko

Proceedings of the Computer Vision - ECCV 2020, 2020

2019

Learning Domain Adaptive Features with Unlabeled Domain Bridges.

[BibT_eX]

[DOI]

Yichen Li

Xingchao Peng

CoRR, 2019

Generalized Domain Adaptation with Covariate and Label Shift CO-ALignment.

[BibT_eX]

[DOI]

Shuhan Tan

Xingchao Peng

Kate Saenko

CoRR, 2019

Domain Agnostic Learning with Disentangled Representations.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

Moment Matching for Multi-Source Domain Adaptation.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

2018

Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation.

[BibT_eX]

[DOI]

CoRR, 2018

Synthetic to Real Adaptation with Generative Correlation Alignment Networks.

[BibT_eX]

[DOI]

Xingchao Peng

Kate Saenko

Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision, 2018

Adapting Control Policies from Simulation to Reality Using a Pairwise Loss.

[BibT_eX]

[DOI]

Proceedings of the 2018 International Symposium on Experimental Robotics, 2018

VisDA: A Synthetic-to-Real Benchmark for Visual Domain Adaptation.

[BibT_eX]

[DOI]

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018

2017

VisDA: The Visual Domain Adaptation Challenge.

[BibT_eX]

[DOI]

CoRR, 2017

Synthetic to Real Adaptation with Deep Generative Correlation Alignment Networks.

[BibT_eX]

[DOI]

Xingchao Peng

Kate Saenko

CoRR, 2017

Ground2sky label transfer for fine-grained aerial car recognition.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Conference on Image Processing, 2017

2016

Fine-to-coarse knowledge transfer for low-res image classification.

[BibT_eX]

[DOI]

Proceedings of the 2016 IEEE International Conference on Image Processing, 2016

Combining Texture and Shape Cues for Object Recognition with Minimal Supervision.

[BibT_eX]

[DOI]

Xingchao Peng

Kate Saenko

Proceedings of the Computer Vision - ACCV 2016, 2016

2015

Towards Adapting Deep Visuomotor Representations from Simulated to Real Environments.

[BibT_eX]

[DOI]

CoRR, 2015

What Do Deep CNNs Learn About Objects?

[BibT_eX]

[DOI]

Proceedings of the 3rd International Conference on Learning Representations, 2015

Learning Deep Object Detectors from 3D Models.

[BibT_eX]

[DOI]

Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

2014

Exploring Invariances in Deep Convolutional Neural Networks Using Synthetic Images.

[BibT_eX]

[DOI]

CoRR, 2014

Xingchao Peng

Timeline

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Links

On csauthors.net:

Bibliography

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