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We investigate the generalization of semi-supervised learning (SSL) to diverse pixel-wise tasks.
Zhu, X.: Semi-supervised learning literature survey (2006)
2006
Earlier work this paper cites.
Hariharan, B., Arbelaez, P., Bourdev, L., Maji, S., Malik, J.: Semantic contours from inverse detectors. In: ICCV (2011)
2011
Earlier work this paper cites.
Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: ICML (2013)
2013
Earlier work this paper cites.
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., Bengio, Y.: Generative adversarial nets. In: NeurIPS (2014)
2014
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: ICLR (2014)
2014
Earlier work this paper cites.
Lin, T.Y., Maire, M., Serge Belongie, J.H., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: ECCV (2014)
2014
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2014)
2014
Earlier work this paper cites.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting. JMLR (2014)
2014
Earlier work this paper cites.
Everingham, M., Eslami, S.M.A., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes challenge: A retrospective. IJCV (2015)
2015
Earlier work this paper cites.
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: ICML (2015)
2015
Earlier work this paper cites.
Rasmus, A., Berglund, M., Honkala, M., Valpola, H., Raiko, T.: Semi-supervised learning with ladder networks. In: NeurIPS (2015)
2015
Earlier work this paper cites.
Springenberg, J.T.: Unsupervised and semi-supervised learning with categorical generative adversarial networks. In: ICLR (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Earlier work this paper cites.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. TPAMI (2016)
2016
Earlier work this paper cites.
Shen, X., Tao, X., Gao, H., Zhou, C., Jia, J.: Deep automatic portrait matting. In: ECCV (2016)
2016
Earlier work this paper 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. TPAMI (2017)
2017
Earlier work this paper cites.
Dai, Z., Yang, Z., Yang, F., Cohen, W.W., Salakhutdinov, R.R.: Good semi-supervised learning that requires a bad gan. In: NeurIPS (2017)
2017
Earlier work this paper cites.
Gharbi, M., Chen, J., Barron, J.T., Hasinoff, S.W., Durand, F.: Deep bilateral learning for real-time image enhancement. In: SIGGRAPH (2017)
2017
Cited alongside, same era.
Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. In: ICLR (2017)
2017
Cited alongside, same era.
LI, C., Xu, T., Zhu, J., Zhang, B.: Triple generative adversarial nets. In: NeurIPS (2017)
2017
Cited alongside, same era.
Mao, X., Li, Q., Xie, H., Lau, R.Y.K., Wang, Z.: Least squares generative adversarial networks. In: ICCV (2017)
2017
Cited alongside, same era.
Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In: NeurIPS (2017)
2017
Cited alongside, same era.
Qiao, S., Shen, W., Zhang, Z., Wang, B., Yuille, A.L.: Deep co-training for semi-supervised image recognition. In: ECCV (2018)
2018
Later among the works it cites.
Zhang, K., Zuo, W., Zhang, L.: Ffdnet: Toward a fast and flexible solution for cnn based image denoising. TIP (2018)
2018
Later among the works it cites.
Abdelhamed, A., Timofte, R., Brown, M.S.: Ntire 2019 challenge on real image denoising: Methods and results. In: CVPRW (2019)
2019
Later among the works it cites.
Anwar, S., Barnes, N.: Real image denoising with feature attention. In: ICCV (2019)
2019
Later among the works it cites.
Athiwaratkun, B., Finzi, M., Izmailov, P., Wilson, A.G.: There are many consistent explanations of unlabeled data: Why you should average. In: ICLR (2019)
2019
Later among the works it cites.
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Xu, N., Price, B.L., Cohen, S., Huang, T.S.: Deep image matting. In: CVPR (2017)
2017
Cited alongside, same era.
Abdelhamed, A., Lin, S., Brown, M.S.: A high-quality denoising dataset for smartphone cameras. In: CVPR (2018)
2018
Cited alongside, same era.
Chen, C., Liu, W., Tan, X., Wong, K.: Semi-supervised learning for face sketch synthesis in the wild. In: ACCV (2018)
2018
Cited alongside, same era.
Chen, C., Chen, Q., Xu, J., Koltun, V.: Learning to see in the dark. In: CVPR (2018)
2018
Cited alongside, same era.
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: ECCV (2018)
2018
Cited alongside, same era.
Huang, Z., Wang, X., Wang, J., Liu, W., Wang, J.: Weakly-supervised semantic segmentation network with deep seeded region growing. In: CVPR (2018)
2018
Cited alongside, same era.
Hung, W.C., Tsai, Y.H., Liou, Y.T., Lin, Y.Y., Yang, M.H.: Adversarial learning for semi-supervised semantic segmentation. In: BMVC (2018)
2018
Cited alongside, same era.
Berthelot, D., Carlini, N., Goodfellow, I.G., Papernot, N., Oliver, A., Raffel, C.: Mixmatch: A holistic approach to semi-supervised learning. In: NeurIPS (2019)
2019
Later among the works it cites.
Guo, S., Yan, Z., Zhang, K., Zuo, W., Zhang, L.: Toward convolutional blind denoising of real photographs. In: CVPR (2019)
2019
Later among the works it cites.
Kalluri, T., Varma, G., Chandraker, M., Jawahar, C.V.: Universal semi-supervised semantic segmentation. In: ICCV (2019)
2019
Later among the works it cites.
Ke, Z., Wang, D., Yan, Q., Ren, J., Lau, R.W.: Dual student: Breaking the limits of the teacher in semi-supervised learning. In: ICCV (2019)
2019
Later among the works it cites.
Lee, J., Kim, E., Lee, S., Lee, J., Yoon, S.: Ficklenet: Weakly and semi-supervised semantic image segmentation using stochastic inference. In: CVPR (2019)
2019
Later among the works it cites.
Mittal, S., Tatarchenko, M., Brox, T.: Semi-supervised semantic segmentation with high- and low-level consistency. TPAMI (2019)
2019
Later among the works it cites.
Park, B., Yu, S., Jeong, J.: Densely connected hierarchical network for image denoising. In: CVPRW (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Wang, Q., Li, W., Van Gool, L.: Semi-supervised learning by augmented distribution alignment. In: ICCV (2019)
2019
Later among the works it cites.
Yu, S., Park, B., Jeong, J.: Deep iterative down-up cnn for image denoising. In: CVPRW (2019)
2019
Later among the works it cites.
Zhai, X., Oliver, A., Kolesnikov, A., Beyer, L.: S4l: Self-supervised semi-supervised learning. In: ICCV (2019)
2019
Later among the works it cites.
Berthelot, D., Carlini, N., Cubuk, E.D., Kurakin, A., Sohn, K., Zhang, H., Raffel, C.: Remixmatch: Semi-supervised learning with distribution matching and augmentation anchoring. In: ICLR (2020)
2020
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