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The ability to understand visual information from limited labeled data is an important aspect of machine learning.
Context encoding for semantic segmentation
H. Zhang, K. Dana, J. Shi, Z. Zhang, X. Wang, A. Tyagi, and A. Agrawal · 1908
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Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. jia Li, K. Li, and L. Fei-fei · 2009
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The pascal visual object classes (voc) challenge
M. Everingham, L. Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
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Semantic contours from inverse detectors
B. Hariharan, P. Arbelaez, L. Bourdev, S. Maji, and J. Malik · 2011
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Rectifier nonlinearities improve neural network acoustic models
A. L. Maas, A. Y. Hannun, and A. Y. Ng · 2013
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Semantic image segmentation with deep convolutional nets and fully connected crfs
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2014
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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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Microsoft COCO: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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The role of context for object detection and semantic segmentation in the wild
R. Mottaghi, X. Chen, X. Liu, N.-G. Cho, S.-W. Lee, S. Fidler, R. Urtasun, and A. Yuille · 2014
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
J. Dai, K. He, and J. Sun · 2015
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Decoupled deep neural network for semi-supervised semantic segmentation
S. Hong, H. Noh, and B. Han · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Weakly- and semi-supervised learning of a deep convolutional network for semantic image segmentation
G. Papandreou, L.-C. Chen, K. P. Murphy, and A. L. Yuille · 2015
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From image-level to pixel-level labeling with convolutional networks
P. O. Pinheiro and R. Collobert · 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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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Scribblesup: Scribble-supervised convolutional networks for semantic segmentation
D. Lin, J. Dai, J. Jia, K. He, and J. Sun · 2016
Semi supervised semantic segmentation using generative adversarial network
N. Souly, C. Spampinato, and M. Shah · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
A. Tarvainen and H. Valpola · 2017
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Object region mining with adversarial erasing: A simple classification to semantic segmentation approach
Y. Wei, J. Feng, X. Liang, M.-M. Cheng, Y. Zhao, and S. Yan · 2017
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Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation
J. Ahn and S. Kwak · 2018
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Improving consistency-based semi-supervised learning with weight averaging
B. Athiwaratkun, M. Finzi, P. Izmailov, and A. G. Wilson · 2018
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Cited alongside, same era.
Semantic segmentation using adversarial networks
P. Luc, C. Couprie, S. Chintala, and J. Verbeek · 2016
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Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, and X. Chen · 2016
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Simple does it: Weakly supervised instance and semantic segmentation
A. Khoreva, R. Benenson, J. Hosang, M. Hein, and B. Schiele · 2017
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Temporal ensembling for semi-supervised learning
S. Laine and T. Aila · 2017
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Refinenet: Multi-path refinement networks for high-resolution semantic segmentation
G. Lin, A. Milan, C. Shen, and I. Reid · 2017
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Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2018
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Encoder-decoder with atrous separable convolution for semantic image segmentation
L. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam · 2018
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Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
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Adversarial learning for semi-supervised semantic segmentation
W. Hung, Y. Tsai, Y. Liou, Y. Lin, and M. Yang · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
T. Miyato, S. ichi Maeda, M. Koyama, and S. Ishii · 2018
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Realistic evaluation of semi-supervised learning algorithms
A. Oliver, A. Odena, C. Raffel, E. D. Cubuk, and I. J. Goodfellow · 2018
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On regularized losses for weakly-supervised cnn segmentation
M. Tang, F. Perazzi, A. Djelouah, I. B. Ayed, C. Schroers, and Y. Boykov · 2018
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Revisiting dilated convolution: A simple approach for weakly- and semi-supervised semantic segmentation
Y. Wei, H. Xiao, H. Shi, Z. Jie, J. Feng, and T. S. Huang · 2018
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