Fetching the paper…
Reading the bibliography…
This paper focuses on the unsupervised domain adaptation of transferring the knowledge from the source domain to the target domain in the context of semantic segmentation.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014) · 1958
Earlier work this paper cites.
Combining labeled and unlabeled data with co-training
Blum, A. and Mitchell, T. (1998) · 1998
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Grandvalet, Y. and Bengio, Y. (2005) · 2005
Earlier work this paper cites.
Bayesian networks and decision graphs
Nielsen, T. D. and Jensen, F. V. (2009) · 2009
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Lee, D.-H. (2013) · 2013
Earlier work this paper cites.
Training deep neural networks on noisy labels with bootstrapping
Reed, S., Lee, H., Anguelov, D., Szegedy, C., Erhan, D., and Rabinovich, A. (2014) · 2014
Earlier work this paper cites.
Transductive multi-view zero-shot learning
Fu, Y., Hospedales, T. M., Xiang, T., and Gong, S. (2015) · 2015
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Ganin, Y. and Lempitsky, V. (2015) · 2015
Earlier work this paper cites.
Simultaneous deep transfer across domains and tasks
Tzeng, E., Hoffman, J., Darrell, T., and Saenko, K. (2015) · 2015
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016) · 2016
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z. (2016) · 2016
Earlier work this paper cites.
Playing for data: Ground truth from computer games
Richter, S. R., Vineet, V., Roth, S., and Koltun, V. (2016) · 2016
Earlier work this paper cites.
The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., and Lopez, A. M. (2016) · 2016
Earlier work this paper cites.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L. (2017) · 2017
Earlier work this paper cites.
What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A. and Gal, Y. (2017) · 2017
Earlier work this paper cites.
Proposal-free network for instance-level object segmentation
Liang, X., Lin, L., Wei, Y., Shen, X., Yang, J., and Yan, S. (2017) · 2017
Cited alongside, same era.
1 Year, 1000km: The Oxford RobotCar Dataset
Maddern, W., Pascoe, G., Linegar, C., and Newman, P. (2017) · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017) · 2017
Cited alongside, same era.
Pyramid scene parsing network
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017) · 2017
Cited alongside, same era.
Cycada: Cycle-consistent adversarial domain adaptation
Hoffman, J., Tzeng, E., Park, T., Zhu, J.-Y., Isola, P., Saenko, K., Efros, A. A., and Darrell, T. (2018) · 2018
Cited alongside, same era.
Domain transfer through deep activation matching
Huang, H., Huang, Q., and Krahenbuhl, P. (2018) · 2018
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T. (2019) · 2019
Later among the works it cites.
Transferrable prototypical networks for unsupervised domain adaptation
Pan, Y., Yao, T., Li, Y., Wang, Y., Ngo, C.-W., and Mei, T. (2019) · 2019
Later among the works it cites.
Domain adaptation for structured output via discriminative representations
Tsai, Y.-H., Sohn, K., Schulter, S., and Chandraker, M. (2019) · 2019
Later among the works it cites.
Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation
Vu, T.-H., Jain, H., Bucher, M., Cord, M., and Pérez, P. (2019) · 2019
Later among the works it cites.
Class-specific reconstruction transfer learning for visual recognition across domains
Wang, S., Zhang, L., Zuo, W., and Zhang, B. (2019) · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Maximum classifier discrepancy for unsupervised domain adaptation
Saito, K., Watanabe, K., Ushiku, Y., and Harada, T. (2018) · 2018
Cited alongside, same era.
Learning to adapt structured output space for semantic segmentation
Tsai, Y.-H., Hung, W.-C., Schulter, S., Sohn, K., Yang, M.-H., and Chandraker, M. (2018) · 2018
Cited alongside, same era.
Transferable joint attribute-identity deep learning for unsupervised person re-identification
Wang, J., Zhu, X., Gong, S., and Li, W. (2018) · 2018
Cited alongside, same era.
Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation
Wei, Y., Xiao, H., Shi, H., Jie, Z., Feng, J., and Huang, T. S. (2018) · 2018
Cited alongside, same era.
Dcan: Dual channel-wise alignment networks for unsupervised scene adaptation
Wu, Z., Han, X., Lin, Y.-L., Gokhan Uzunbas, M., Goldstein, T., Nam Lim, S., and Davis, L. S. (2018) · 2018
Cited alongside, same era.
Fully convolutional adaptation networks for semantic segmentation
Zhang, Y., Qiu, Z., Yao, T., Liu, D., and Mei, T. (2018) · 2018
Cited alongside, same era.
Wu, Z., Wang, X., Gonzalez, J. E., Goldstein, T., and Davis, L. S. (2019) · 2019
Later among the works it cites.
Robust person re-identification by modelling feature uncertainty
Yu, T., Li, D., Yang, Y., Hospedales, T. M., and Xiang, T. (2019) · 2019
Later among the works it cites.
Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data
Yue, X., Zhang, Y., Zhao, S., Sangiovanni-Vincentelli, A., Keutzer, K., and Gong, B. (2019) · 2019
Later among the works it cites.
Confidence regularized self-training
Zou, Y., Yu, Z., Liu, X., Kumar, B., and Wang, J. (2019) · 2019
Later among the works it cites.
Pixel-level cycle association: A new perspective for domain adaptive semantic segmentation
Kang, G., Wei, Y., Yang, Y., and Hauptmann, A. (2020) · 2020
Closest in time.
Adversarial style mining for one-shot unsupervised domain adaptation
Luo, Y., Liu, P., Guan, T., Yu, J., and Yang, Y. (2020) · 2020
Closest in time.
An adversarial perturbation oriented domain adaptation approach for semantic segmentation
Yang, J., Xu, R., Li, R., Qi, X., Shen, X., Li, G., and Lin, L. (2020) · 2020
Closest in time.
Dynamic graph message passing network
Zhang, L., Xu, D., Arnab, A., and Torr, P. H. (2020) · 2020
Closest in time.
Unsupervised scene adaptation with memory regularization in vivo
Zheng, Z. and Yang, Y. (2020) · 2020
Closest in time.