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Domain adaptation of visual detectors is a critical challenge, yet existing methods have overlooked pixel appearance transformations, focusing instead on bootstrapping and/or domain confusion losses.
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A. Torralba and A. A. Efros · 2011
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Semi-supervised domain adaptation with instance constraints
J. Donahue, J. Hoffman, E. Rodner, K. Saenko, and T. Darrell · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Deep generative image models using a laplacian pyramid of adversarial networks
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Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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M. Long and J. Wang · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
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Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Simultaneous deep transfer across domains and tasks
E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko · 2015
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The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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V. Dumoulin, I. Belghazi, B. Poole, A. Lamb, M. Arjovsky, O. Mastropietro, and A. Courville · 2016
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Deep reconstruction-classification networks for unsupervised domain adaptation
M. Ghifary, W. B. Kleijn, M. Zhang, D. Balduzzi, and W. Li · 2016
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Coupled generative adversarial networks
M.-Y. Liu and O. Tuzel · 2016
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Playing for data: Ground truth from computer games
S. R. Richter, V. Vineet, S. Roth, and V. Koltun · 2016
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Improved training of wasserstein gans
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville · 2017
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Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks?
M. Johnson-Roberson, C. Barto, R. Mehta, S. N. Sridhar, K. Rosaen, and R. Vasudevan · 2017
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Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
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Least squares generative adversarial networks
X. Mao, Q. Li, H. Xie, R. Y. Lau, Z. Wang, and S. Paul Smolley · 2017
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Learning from simulated and unsupervised images through adversarial training
A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, and R. Webb · 2017
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Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
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T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Deep CORAL: correlation alignment for deep domain adaptation
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Energy-based generative adversarial network
J. Zhao, M. Mathieu, and Y. LeCun · 2016
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M. Arjovsky, S. Chintala, and L. Bottou · 2017
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K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan · 2017
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Adversarial feature learning
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Unpaired image-to-image translation using cycle-consistent adversarial networks
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Domain adaptive faster r-cnn for object detection in the wild
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