Fetching the paper…
Reading the bibliography…
Current adversarial adaptation methods attempt to align the cross-domain features, whereas two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias of the decision boundary towards the source domain.
Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: NeurIPS (2005)
2005
Earlier work this paper cites.
Maaten, L.v.d., Hinton, G.: Visualizing data using t-sne. JMLR 9
2008
Earlier work this paper cites.
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Vaughan, J.W.: A theory of learning from different domains. Machine learning 79
2010
Earlier work this paper cites.
Gopalan, R., Li, R., Chellappa, R.: Domain adaptation for object recognition: An unsupervised approach. In: ICCV (2011)
2011
Earlier work this paper cites.
Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: CVPR (2012)
2012
Earlier work this paper cites.
Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer feature learning with joint distribution adaptation. In: ICCV (2013)
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Sun, B., Saenko, K.: Subspace distribution alignment for unsupervised domain adaptation. In: BMVC (2015)
2015
Earlier work this paper cites.
Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: Simultaneous deep transfer across domains and tasks. In: ICCV (2015)
2015
Earlier work this paper cites.
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
Earlier work this paper cites.
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
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.
Khan, M.N.A., Heisterkamp, D.R.: Adapting instance weights for unsupervised domain adaptation using quadratic mutual information and subspace learning. In: ICPR (2016)
2016
Cited alongside, same era.
Sun, B., Saenko, K.: Deep coral: Correlation alignment for deep domain adaptation. In: ECCV (2016)
2016
Cited alongside, same era.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: ICCV (2017)
2017
Cited alongside, same era.
Jing, H., Smola, A.J.: Neural survival recommender. In: WSDM (2017)
2017
Cited alongside, same era.
Motiian, S., Jones, Q., Iranmanesh, S., Doretto, G.: Few-shot adversarial domain adaptation. In: NeurIPS (2017)
2017
Cited alongside, same era.
Saito, K., Watanabe, K., Ushiku, Y., Harada, T.: Maximum classifier discrepancy for unsupervised domain adaptation. In: CVPR (2018)
2018
Later among the works it cites.
2018
Later among the works it cites.
Wang, J., Feng, W., Chen, Y., Yu, H., Huang, M., Yu, P.S.: Visual domain adaptation with manifold embedded distribution alignment. In: ACM MM (2018)
2018
Later among the works it cites.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: NeurIPS (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…
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Saito, K., Ushiku, Y., Harada, T.: Asymmetric tri-training for unsupervised domain adaptation. In: ICML (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: CVPR (2017)
2017
Cited alongside, same era.
Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep hashing network for unsupervised domain adaptation. In: CVPR (2017)
2017
Cited alongside, same era.
Cao, Y., Long, M., Wang, J.: Unsupervised domain adaptation with distribution matching machines. In: AAAI (2018)
2018
Cited alongside, same era.
Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., Wang, B.: Moment matching for multi-source domain adaptation. In: ICCV (2019)
2019
Later among the works it cites.
Qin, C., Wang, L., Zhang, Y., Fu, Y.: Generatively inferential co-training for unsupervised domain adaptation. In: ICCV Workshops (2019)
2019
Later among the works it cites.
Qin, C., You, H., Wang, L., Kuo, C.C.J., Fu, Y.: Pointdan: A multi-scale 3d domain adaption network for point cloud representation. In: NeurIPS (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Cheng, L., Guo, R., Candan, K.S., Liu, H.: Representation learning for imbalanced cross-domain classification. In: SDM (2020)
2020
Closest in time.
2020
Closest in time.
Dong, J., Cong, Y., Sun, G., Liu, Y., Xu, X.: Cscl: Critical semantic-consistent learning for unsupervised domain adaptation. In: ECCV (2020)
2020
Closest in time.
Dong, J., Cong, Y., Sun, G., Zhong, B., Xu, X.: What can be transferred: Unsupervised domain adaptation for endoscopic lesions segmentation. In: CVPR (2020)
2020
Closest in time.
Zhang, Y., Zhang, Y., Wei, Y., Bai, K., Song, Y., Yang, Q.: Fisher deep domain adaptation. In: SDM (2020)
2020
Closest in time.