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Unsupervised domain adaptation (UDA) assumes that source and target domain data are freely available and usually trained together to reduce the domain gap.
Empirical Upper Bound in Object Detection and More
Borji, A.; and Iranmanesh, S. M. 2019 · 1911
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Gradient-based learning applied to document recognition
Lecun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998 · 1998
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The MNIST database of handwritten digits
LeCun, Y.; Cortes, C.; and Burges, C. 1998 · 1998
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YOLOv4: Optimal Speed and Accuracy of Object Detection
Bochkovskiy, A.; Wang, C.-Y.; and Liao, H.-Y. M. 2020 · 2004
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Domain Adaptation without Source Data
Kim, Y.; Hong, S.; and Cho, D. 2020 · 2007
Earlier work this paper cites.
Mixture regression for covariate shift
Sugiyama, M.; and Storkey, A. 2007 · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A.; Lenz, P.; and Urtasun, R. 2012 · 2012
Earlier work this paper cites.
Learning to label aerial images from noisy data
Mnih, V.; and Hinton, G. E. 2012 · 2012
Earlier work this paper cites.
Learning with noisy labels
Natarajan, N.; Dhillon, I. S.; Ravikumar, P. K.; and Tewari, A. 2013 · 2013
Earlier work this paper cites.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2015 · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K.; and Zisserman, A. 2015 · 2015
Earlier work this paper cites.
The Cityscapes dataset for semantic urban scene understanding
Cordts, M.; Omran, M.; Ramos, S.; Rehfeld, T.; Enzweiler, M.; Benenson, R.; Franke, U.; Roth, S.; and Schiele, B. 2016 · 2016
Earlier work this paper cites.
SSD: Single Shot MultiBox Detector
Liu, W.; Anguelov, D.; Erhan, D.; Szegedy, C.; Reed, S.; Fu, C.-Y.; and Berg, A. C. 2016 · 2016
Cited alongside, same era.
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J.; Divvala, S.; Girshick, R.; and Farhadi, A. 2016 · 2016
Cited alongside, same era.
Driving in the matrix: Can virtual worlds replace humangenerated annotations for real world tasks?
Johnson-Roberson, M.; Barto, C.; Mehta, R.; Sridhar, S. N.; Rosaen, K.; and Vasudevan, R. 2017 · 2017
Cited alongside, same era.
Unsupervised image-to-image translation networks
Liu, M.-Y.; Breuel, T.; and Kautz, J. 2017 · 2017
Cited alongside, same era.
Making neural networks robust to label noise: A loss correction approach
Patrini, G.; Rozza, A.; Menon, A.; Nock, R.; and Qu, L. 2017 · 2017
Cited alongside, same era.
Toward robustness against label noise in training deep discriminative neural networks
Progressive Feature Alignment for Unsupervised Domain Adaptation
C.Chen; W.Xie; W.Huang; Y.Rong; X.Ding; Y.Huang; T.Xu; and J.Huang. 2019 · 2019
Later among the works it cites.
A Robust Learning Approach to Domain Adaptive Object Detection
Khodabandeh, M.; Vahdat, A.; Ranjbar, M.; and Macready, W. G. 2019 · 2019
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Strong-Weak Distribution Alignment for Adaptive Object Detection
Saito, K.; Ushiku, Y.; Harada, T.; and Saenko, K. 2019 · 2019
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CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Yun, S.; Han, D.; Oh, S. J.; Chun, S.; Choe, J.; and Yoo, Y. 2019 · 2019
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Multi-adversarial Faster-RCNN for Unrestricted Object Detection
Z.He; and L.Zhang. 2019 · 2019
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Adapting Object Detectors via Selective Cross-Domain Alignment
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Vahdat, A. 2017 · 2017
Cited alongside, same era.
Learning from noisy large-scale datasets with minimal supervision
Veit, A.; Alldrin, N.; Chechik, G.; Krasin, I.; Gupta, A.; and Belongie, S. 2017 · 2017
Cited alongside, same era.
Domain Adaptive Faster R-CNN for Object Detection in the Wild
Chen, Y.; Li, W.; Sakaridis, C.; Dai, D.; and Gool, L. V. 2018 · 2018
Cited alongside, same era.
Cycada: Cycle-consistent adversarial domain adaptation
Hoffman, J.; Tzeng, E.; Park, T.; Zhu, J.-Y.; Isola, P.; Saenko, K.; Efros, A. A.; and Darrell, T. 2018 · 2018
Cited alongside, same era.
Mentornet: Regularizing very deep neural networks on corrupted labels
Jiang, L.; Zhou, Z.; Leung, T.; Li, L.-J.; and Fei-Fei, L. 2018 · 2018
Cited alongside, same era.
Learning to reweight examples for robust deep learning
Ren, M.; Zeng, W.; Yang, B.; and Urtasun, R. 2018 · 2018
Cited alongside, same era.
Semantic foggy scene understanding with synthetic data
Sakaridis, C.; Dai, D.; and Gool, L. V. 2018 · 2018
Cited alongside, same era.
Zhu, X.; Pang, J.; Yang, C.; Shi, J.; and Lin, D. 2019 · 2019
Later among the works it cites.
Domain Adaptive Object Detection via Asymmetric Tri-way Faster-RCNN
He, Z.; and Zhang, L. 2020 · 2020
Closest in time.
Progressive Domain Adaptation for Object Detection
Hsu, H.-K.; Yao, C.-H.; Tsai, Y.-H.; Hung, W.-C.; Tseng, H.; Singh, M.; and Yang, M.-H. 2020 · 2020
Closest in time.
Model Adaptation: Unsupervised Domain Adaptation without Source Data
Li, R.; Jiao, Q.; Cao, W.; Wong, H.-S.; and Wu, S. 2020 · 2020
Closest in time.
Federated Adversarial Domain Adaptation
Peng, X.; Huang, Z.; Zhu, Y.; and Saenko, K. 2020 · 2020
Closest in time.
Unsupervised domain adaptation via structured prediction based selective pseudo-labeling
Q.Wang; and T.Breckon. 2020 · 2020
Closest in time.
Exploring Categorical Regularization for Domain Adaptive Object Detection
Xu, C.; Zhao, X.; Jin, X.; and Wei, X. 2020 · 2020
Closest in time.