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曹艺 等: 融合扩增技术的无监督域适应方法 3269
[40] Zhang Z, Wang MZ, Huang Y, Nehorai A. Aligning infinite-dimensional covariance matrices in reproducing kernel Hilbert spaces for
domain adaptation. In: Proc. of the 2018 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018.
3437–3445. [doi: 10.1109/CVPR.2018.00362]
[41] Bousmalis K, Trigeorgis G, Silberman N, Krishnan D, Erhan D. Domain separation networks. In: Proc. of the 30th Int’l Conf. on Neural
Information Processing Systems. Barcelona: Curran Associates Inc., 2016. 343–351.
[42] Cicek S, Soatto S. Unsupervised domain adaptation via regularized conditional alignment. In: Proc. of the 2019 IEEE/CVF Int’l Conf. on
Computer Vision. Seoul: IEEE, 2019. 1416–1425. [doi: 10.1109/ICCV.2019.00150]
[43] Vu TH, Jain H, Bucher M, Cord M, Pérez P. ADVENT: Adversarial entropy minimization for domain adaptation in semantic
segmentation. In: Proc. of the 2019 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019. 2512–2521.
[doi: 10.1109/CVPR.2019.00262]
[44] Tzeng E, Hoffman J, Saenko K, Darrell T. Adversarial discriminative domain adaptation. In: Proc. of the 2017 IEEE Conf. on Computer
Vision and Pattern Recognition. Honolulu: IEEE, 2017. 2962–2971. [doi: 10.1109/CVPR.2017.316]
[45] Liu MY, Tuzel O. Coupled generative adversarial networks. In: Proc. of the 30th Int’l Conf. on Neural Information Processing Systems.
Barcelona: Curran Associates Inc., 2016. 469–477.
[46] Ghifary M, Kleijn WB, Zhang MJ, Balduzzi D, Li W. Deep reconstruction-classification networks for unsupervised domain adaptation.
In: Proc. of the 14th European Conf. on Computer Vision. Amsterdam: Springer, 2016. 597–613. [doi: 10.1007/978-3-319-46493-0_36]
[47] Zhang Y, Chen RR, Zhang J. Safe Tri-training algorithm based on cross entropy. Journal of Computer Research and Development, 2021,
58(1): 60–69 (in Chinese with English abstract). [doi: 10.7544/issn1000-1239.2021.20190838]
[48] Bošnjak M, Richemond PH, Tomasev N, Strub F, Walker JC, Hill F, Buesing LH, Pascanu R, Blundell C, Mitrovic J. SemPPL:
Predicting pseudo-labels for better contrastive representations. In: Proc. of the 11th Int’l Conf. on Learning Representations. Kigali:
OpenReview.net, 2023.
[49] Shi WW, Gong YH, Ding C, Ma ZH, Tao XY, Zheng NN. Transductive semi-supervised deep learning using min-max features. In:
Ferrari V, Hebert M, eds. Proc. of the 15th European Conf. on Computer Vision. Munich: Springer, 2018. 311–327. [doi: 10.1007/978-3-
030-01228-1_19]
[50] Rizve MN, Duarte K, Rawat YS, Shah M. In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for
semi-supervised learning. In: Proc. of the 2021 Int’l Conf. on Learning Representations. Vienna: OpenReview.net, 2021. 1–20.
[51] Laine S, Aila T. Temporal ensembling for semi-supervised learning. In: Proc. of the 5th Int’l Conf. on Learning Representations. Toulon:
OpenReview.net, 2016.
[52] Cubuk ED, Zoph B, Mané D, Vasudevan V, Le QV. AutoAugment: Learning augmentation strategies from data. In: Proc. of the 2019
IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019. 113–123. [doi: 10.1109/CVPR.2019.00020]
[53] Xie QZ, Dai ZH, Hovy E, Luong MT, Le QV. Unsupervised data augmentation for consistency training. In: Proc. of the 34th Int’l Conf.
on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2020. 525.
[54] Berthelot D, Carlini N, Goodfellow I, Oliver A, Papernot N, Raffel CA. MixMatch: A holistic approach to semi-supervised learning. In:
Proc. of the 33rd Int’l Conf. on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2019. 454.
[55] Miyato T, Maeda SI, Koyama M, Ishii S. Virtual adversarial training: A regularization method for supervised and semi-supervised
learning. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2019, 41(8): 1979–1993. [doi: 10.1109/TPAMI.2018.2858821]
[56] Tarvainen A, Valpola H. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep
learning results. In: Proc. of the 31st Int’l Conf. on Neural Information Processing Systems. Long Beach: Curran Associates Inc., 2017.
1195–1204.
[57] LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc. of the IEEE, 1998, 86(11):
2278–2324. [doi: 10.1109/5.726791]
[58] Hull JJ. A database for handwritten text recognition research. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1994, 16(5):
550–554. [doi: 10.1109/34.291440]
[59] Netzer Y, Wang T, Coates A, Bissacco A, Wu B, Ng AY. Reading digits in natural images with unsupervised feature learning. In: Proc.
of the 2011 NIPS Workshop on Deep Learning and Unsupervised Feature Learning. Granada: NIPS, 2011. 5–13.
[60] Venkateswara H, Eusebio J, Chakraborty S, Panchanathan S. Deep hashing network for unsupervised domain adaptation. In: Proc. of the
2017 IEEE Conf. on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017. 5385–5394. [doi: 10.1109/CVPR.2017.572]
[61] Saito K, Watanabe K, Ushiku Y, Harada T. Maximum classifier discrepancy for unsupervised domain adaptation. In: Proc. of the 2018
IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018. 3723–3732. [doi: 10.1109/CVPR.2018.
00392]

