Fetching the paper…
Reading the bibliography…
Unsupervised domain adaptation (UDA) aims to adapt existing models of the source domain to a new target domain with only unlabeled data.
Bayesian uncertainty matching for unsupervised domain adaptation
Wen, J., Zheng, N., Yuan, J., Gong, Z., Chen, C., 2019 · 1906
Earlier work this paper cites.
All about structure: Adapting structural information across domains for boosting semantic segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1900–1909
Chang, W.L., Wang, H.P., Peng, W.H., Chiu, W.C., 2019 · 1909
Earlier work this paper cites.
CyCADA: Cycle-consistent adversarial domain adaptation, in: International conference on machine learning, pp. 1989–1998
Hoffman, J., Tzeng, E., Park, T., Zhu, J.Y., Isola, P., Saenko, K., Efros, A., Darrell, T., 2018 · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., 1998 · 1998
Earlier work this paper cites.
No more discrimination: Cross city adaptation of road scene segmenters, in: Proceedings of the IEEE International Conference on Computer Vision, pp. 1992–2001
Chen, Y.H., Chen, W.Y., Chen, Y.T., Tsai, B.C., Frank Wang, Y.C., Sun, M., 2017 · 2001
Earlier work this paper cites.
Milking cowmask for semi-supervised image classification
French, G., Oliver, A., Salimans, T., 2020b · 2003
Earlier work this paper cites.
Semi-supervised semantic segmentation via dynamic self-training and class-balanced curriculum
Feng, Z., Zhou, Q., Cheng, G., Tan, X., Shi, J., Ma, L., 2020 · 2004
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database, in: CVPR
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L., 2009 · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A., 2010 · 2010
Earlier work this paper cites.
Daml: Domain adaptation metric learning
Geng, B., Tao, D., Xu, C., 2011 · 2011
Earlier work this paper cites.
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms, in: CVPR
Kulis, B., Saenko, K., Darrell, T., 2011 · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning, in: NeurIPS workshop
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y., 2011 · 2011
Earlier work this paper cites.
Geodesic flow kernel for unsupervised domain adaptation, in: CVPR
Gong, B., Shi, Y., Sha, F., Grauman, K., 2012 · 2012
Earlier work this paper cites.
Unsupervised visual domain adaptation using subspace alignment, in: ICCV
Fernando, B., Habrard, A., Sebban, M., Tuytelaars, T., 2013 · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y., 2014 · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context, in: European conference on computer vision, Springer. pp. 740–755
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L., 2014 · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Andrew, Z., 2014 · 2014
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation, in: ICLR
Ganin, Y., Lempitsky, V., 2015 · 2015
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding, in: Proc. CVPR, pp. 3213–3223
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B., 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
Earlier work this paper cites.
Fcns in the wild: Pixel-level adversarial and constraint-based adaptation
Hoffman, J., Wang, D., Yu, F., Darrell, T., 2016 · 2016
Earlier work this paper cites.
Temporal ensembling for semi-supervised learning
Laine, S., Aila, T., 2016 · 2016
Earlier work this paper cites.
Playing for data: Ground truth from computer games, in: ECCV
Richter, S.R., Vineet, V., Roth, S., Koltun, V., 2016 · 2016
Cited alongside, same era.
The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes, in: CVPR
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.M., 2016 · 2016
Cited alongside, same era.
Return of frustratingly easy domain adaptation, in: AAAI
Sun, B., Feng, J., Saenko, K., 2016 · 2016
Cited alongside, same era.
Autodial: Automatic domain alignment layers, in: ICCV
Cariucci, F.M., Porzi, L., Caputo, B., Ricci, E., Bulo, S.R., 2017 · 2017
Cited alongside, same era.
Domain adaptation for visual applications: A comprehensive survey
Csurka, G., 2017 · 2017
Cited alongside, same era.
Domain adaptation for structured output via discriminative patch representations, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1456–1465
Tsai, Y.H., Sohn, K., Schulter, S., Chandraker, M., 2019 · 2019
Later among the works it cites.
Self-ensembling attention networks: Addressing domain shift for semantic segmentation, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 5581–5588
Xu, Y., Du, B., Zhang, L., Zhang, Q., Wang, G., Zhang, L., 2019 · 2019
Later among the works it cites.
Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation, in: MICCAI
Yu, L., Wang, S., Li, X., Fu, C.W., Heng, P.A., 2019 · 2019
Later among the works it cites.
Category anchor-guided unsupervised domain adaptation for semantic segmentation, in: Advances in Neural Information Processing Systems, pp. 433–443
Zhang, Q., Zhang, J., Liu, W., Tao, D., 2019 · 2019
Later among the works it cites.
Multi-source domain adaptation for semantic segmentation, in: NeurIPS
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kendall, A., Gal, Y., 2017 · 2017
Cited alongside, same era.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results, in: Advances in Neural Information Processing Systems 30, pp. 1195–1204
Tarvainen, A., Valpola, H., 2017 · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks, in: Proceedings of the IEEE international conference on computer vision, pp. 2223–2232
Zhu, J.Y., Park, T., Isola, P., Efros, A.A., 2017 · 2017
Cited alongside, same era.
Self-ensembling for visual domain adaptation, in: Proceedings of the International Conference on Learning Representations
French, G., Mackiewicz, M., Fisher, M., 2018 · 2018
Cited alongside, same era.
Maximum classifier discrepancy for unsupervised domain adaptation, in: CVPR
Saito, K., Watanabe, K., Ushiku, Y., Harada, T., 2018 · 2018
Cited alongside, same era.
Learning from synthetic data: Addressing domain shift for semantic segmentation, in: CVPR
Sankaranarayanan, S., Balaji, Y., Jain, A., Nam Lim, S., Chellappa, R., 2018 · 2018
Cited alongside, same era.
Learning to adapt structured output space for semantic segmentation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7472–7481
Tsai, Y.H., Hung, W.C., Schulter, S., Sohn, K., Yang, M.H., Chandraker, M., 2018 · 2018
Cited alongside, same era.
Zhao, S., Li, B., Yue, X., Gu, Y., Xu, P., Tan, Hu, R., Chai, H., Keutzer, K., 2019 · 2019
Later among the works it cites.
Confidence regularized self-training, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5982–5991
Zou, Y., Yu, Z., Liu, X., Kumar, B., Wang, J., 2019 · 2019
Later among the works it cites.
Contextual-relation consistent domain adaptation for semantic segmentation, in: European conference on computer vision, pp. 705–722
Huang, J., Lu, S., Guan, D., Zhang, X., 2020 · 2020
Closest in time.
Learning texture invariant representation for domain adaptation of semantic segmentation, in: Proc. CVPR, pp. 12975–12984
Kim, M., Byun, H., 2020 · 2020
Closest in time.
Stochastic classifiers for unsupervised domain adaptation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9111–9120
Lu, Z., Yang, Y., Zhu, X., Liu, C., Song, Y.Z., Xiang, T., 2020 · 2020
Closest in time.
Learning from scale-invariant examples for domain adaptation in semantic segmentation, in: European conference on computer vision, Springer. pp. 290–306
Naseer Subhani, M., Ali, M., 2020 · 2020
Closest in time.
Unsupervised intra-domain adaptation for semantic segmentation through self-supervision, in: Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision, pp. 3764–3773
Pan, F., Shin, I., Rameau, F., Lee, S., Kweon, I.S., 2020 · 2020
Closest in time.
Domain adaptive semantic segmentation using weak labels, in: European conference on computer vision, Springer. pp. 571–587
Paul, S., Tsai, Y., Schulter, S., Roy-Chowdhury, A.K., Chandraker, M., 2020 · 2020
Closest in time.
Fda: Fourier domain adaptation for semantic segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4085–4095
Yang, Y., Soatto, S., 2020 · 2020
Closest in time.
Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation
Zheng, Z., Yang, Y., 2020 · 2020
Closest in time.
Affinity space adaptation for semantic segmentation across domains
Zhou, W., Wang, Y., Chu, J., Yang, J., Bai, X., Xu, Y., 2020 · 2020
Closest in time.
Pit: Position-invariant transform for cross-fov domain adaptation, in: Proceedings of the IEEE/CVF International Conference on Computer Vision
Gu, Q., Zhou, Q., Xu, M., Feng, Z., Cheng, G., Lu, X., Shi, J., Ma, L., 2021 · 2021
Closest in time.
Category-level adversarial adaptation for semantic segmentation using purified features
Luo, Y., Liu, P., Zheng, L., Guan, T., Yu, J., Yang, Y., 2021 · 2021
Closest in time.
Dacs: Domain adaptation via cross-domain mixed sampling, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1379–1389
Tranheden, W., Olsson, V., Pinto, J., Svensson, L., 2021 · 2021
Closest in time.
Context-aware domain adaptation in semantic segmentation, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 514–524
Yang, J., An, W., Yan, C., Zhao, P., Huang, J., 2021 · 2021
Closest in time.
Dast: Unsupervised domain adaptation in semantic segmentation based on discriminator attention and self-training, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 10754–10762
Yu, F., Zhang, M., Dong, H., Hu, S., Dong, B., Zhang, L., 2021 · 2021
Closest in time.
Curriculum domain adaptation for semantic segmentation of urban scenes, in: Proceedings of the IEEE international conference on computer vision, pp. 2020–2030
Zhang, Y., David, P., Gong, B., 2017 · 2030
Closest in time.