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We propose a normalization layer for unsupervised domain adaption in semantic scene segmentation.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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A theory of learning from different domains
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, J. Wortman Vaughan, S. R. Ben-David David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Vaughan · 2010
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Unbiased look at dataset bias
A. Torralba and A. A. Efros · 2011
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Undoing the damage of dataset bias
A. Khosla, T. Zhou, T. Malisiewicz, A. A. Efros, and A. Torralba · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Representation Learning: A Review and New Perspectives
Y. Bengio, A. Courville, and P. Vincent · 2013
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Learning hierarchical features for scene labeling
C. Farabet, C. Couprie, L. Najman, and Y. LeCun · 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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Simultaneous detection and segmentation
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2014
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Fully Convolutional Multi-Class Multiple Instance Learning
D. Pathak, E. Shelhamer, J. Long, and T. Darrell · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
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A deeper look at dataset bias
T. Tommasi, N. Patricia, B. Caputo, and T. Tuytelaars · 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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Multi-Scale Context Aggregation by Dilated Convolutions
F. Yu and V. Koltun · 2015
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, L. Kaiser, M. Kudlur, J. Levenberg, D. Man, R. Monga, S. Moore, D. Murray, J. Shlens, B. Steiner, I. Sutskever, P. Tucker, V. Vanhoucke, V. Vasudevan, O. Vinyals, P. Warden, M. Wicke, Y. Yu, and X. Zheng · 2016
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Layer normalization
J. L. Ba, J. R. Kiros, and G. E. Hinton · 2016
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How to train a GAN, Workshop on Adversarial Training at NIPS 2016, 2016
S. Chintala · 2016
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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
Cited alongside, same era.
Domain-Adversarial Training of Neural Networks
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky · 2017
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Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2017
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Revisiting Batch Normalization For Practical Domain Adaptation
Y. Li, N. Wang, J. Shi, J. Liu, and X. Hou · 2017
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Learning for Disparity Estimation through Feature Constancy
Z. Liang, Y. Feng, Y. Guo, H. Liu, L. Qiao, W. Chen, L. Zhou, and J. Zhang · 2017
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The mapillary vistas dataset for semantic understanding of street scenes
G. Neuhold, T. Ollmann, S. R. Bulò, and P. Kontschieder · 2017
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Unsupervised Domain Adaptation for Semantic Segmentation with GANs
S. Sankaranarayanan, Y. Balaji, A. Jain, S. Lim, and R. Chellappa · 2017
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K. He, X. Zhang, S. Ren, and J. Sun · 2016
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FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
J. Hoffman, D. Wang, F. Yu, and T. Darrell · 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 techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
Cited alongside, same era.
Instance Normalization: The Missing Ingredient for Fast Stylization
D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2016
Cited alongside, same era.
Towards principled methods for training generative adversarial networks
M. Arjovsky and L. Bottou · 2017
Cited alongside, same era.
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Adversarial Discriminative Domain Adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
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On the Effects of Batch and Weight Normalization in Generative Adversarial Networks
S. Xiang and H. Li · 2017
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
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Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes
Y. Zhang, P. David, and B. Gong · 2017
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Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
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Understanding Batch Normalization
J. Bjorck, C. Gomes, and B. Selman · 2018
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The ApolloScape Dataset for Autonomous Driving
X. Huang, X. Cheng, Q. Geng, B. Cao, D. Zhou, P. Wang, Y. Lin, and R. Yang · 2018
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Towards a Theoretical Understanding of Batch Normalization
J. Kohler, H. Daneshmand, A. Lucchi, M. Zhou, K. Neymeyr, and T. Hofmann · 2018
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How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift)
S. Santurkar, D. Tsipras, A. Ilyas, and A. Madry · 2018
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Removed for blind review
U. Unknown · 2018
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