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Recent deep networks achieved state of the art performance on a variety of semantic segmentation tasks.
Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods. In: ACL (1995)
1995
Earlier work this paper cites.
Riloff, E., Wiebe, J., Wilson, T.: Learning subjective nouns using extraction pattern bootstrapping. In: Seventh conference on Natural language learning, HLT-NAACL Workshop (2003)
2003
Earlier work this paper cites.
Maeireizo, B., Litman, D., Hwa, R.: Co-training for predicting emotions with spoken dialogue data. In: Interactive poster and demonstration sessions, ACL (2004)
2004
Earlier work this paper cites.
Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: NIPS (2005)
2005
Earlier work this paper cites.
Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews]. IEEE Trans. Neural Networks 20
2009
Earlier work this paper cites.
Chen, M., Weinberger, K.Q., Blitzer, J.: Co-training for domain adaptation. In: NIPS (2011)
2011
Earlier work this paper cites.
Silberman, N., Fergus, R.: Indoor scene segmentation using a structured light sensor. In: ICCV Workshop on 3D Representation and Recognition (2011)
2011
Earlier work this paper cites.
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: CVPR (2012)
2012
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)
2012
Earlier work this paper cites.
Tang, K., Ramanathan, V., Fei-Fei, L., Koller, D.: Shifting weights: Adapting object detectors from image to video. In: NIPS (2012)
2012
Earlier work this paper cites.
Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M.: Domain-adversarial neural networks. In: NIPS Workshop on Transfer and Multi-task learning: Theory Meets Practice (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: ICML (2015)
2015
Earlier work this paper cites.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)
2015
Earlier work this paper cites.
Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: ICML (2015)
2015
Cited alongside, same era.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. IJCV 115
2015
Cited alongside, same era.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
2015
Cited alongside, same era.
Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: Simultaneous deep transfer across domains and tasks. In: ICCV (2015)
2015
Cited alongside, same era.
Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., Erhan, D.: Domain separation networks. In: NIPS (2016)
2016
Cited alongside, same era.
2017
Later among the works it cites.
Chen, Y.H., Chen, W.Y., Chen, Y.T., Tsai, B.C., Frank Wang, Y.C., Sun, M.: No more discrimination: Cross city adaptation of road scene segmenters. In: ICCV (2017)
2017
Later among the works it cites.
Richter, S.R., Hayder, Z., Koltun, V.: Playing for benchmarks. In: ICCV (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
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Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: CVPR (2016)
2016
Cited alongside, same era.
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: Domain-adversarial training of neural networks. JMLR 17
2016
Cited alongside, same era.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Richter, S.R., Vineet, V., Roth, S., Koltun, V.: Playing for data: Ground truth from computer games. In: ECCV (2016)
2016
Cited alongside, same era.
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.M.: The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes. In: CVPR (2016)
2016
Cited alongside, same era.
Sun, B., Saenko, K.: Deep coral: Correlation alignment for deep domain adaptation. In: ECCV Workshops (2016)
2016
Cited alongside, same era.
2017
Later among the works it cites.
Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: Adversarial discriminative domain adaptation. In: CVPR (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: CVPR (2017)
2017
Later among the works it cites.
Zhang, Y., David, P., Gong, B.: Curriculum domain adaptation for semantic segmentation of urban scenes. In: ICCV (2017)
2017
Later among the works it cites.
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR (2017)
2017
Later among the works it cites.
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. PAMI 40
2018
Closest in time.
Hoffman, J., Tzeng, E., Park, T., Zhu, J.Y., Isola, P., Saenko, K., Efros, A.A., Darrell, T.: Cycada: Cycle-consistent adversarial domain adaptation. In: ICML (2018)
2018
Closest in time.
Murez, Z., Kolouri, S., Kriegman, D., Ramamoorthi, R., Kim, K.: Image to image translation for domain adaptation. In: CVPR (2018)
2018
Closest in time.
Tsai, Y.H., Hung, W.C., Schulter, S., Sohn, K., Yang, M.H., Chandraker, M.: Learning to adapt structured output space for semantic segmentation. CVPR (2018)
2018
Closest in time.
Zhu, X.: Semi-supervised learning literature survey. Computer Science, University of Wisconsin-Madison 2
2018
Closest in time.