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王帆 等: 局部一致性主动学习的源域无关开集域自适应 1665

[21] Yuan Y, Chung SW, Kang HG. Gradient-based active learning query strategy for end-to-end speech recognition. In: Proc. of the
IEEE Int’l Conf. on Acoustics, Speech and Signal Processing (ICASSP). Brighton: IEEE, 2019. 2832−2836.
[22] Yang JF, Peng XY, Wang K, et al. Divide to adapt: Mitigating confirmation bias for domain adaptation of black-box predictors.
arXiv:2205.14467, 2022.
[23] Tian Q, Ma C, Zhang FY, et al. Source-free unsupervised domain adaptation with sample transport learning. Computer Science and
Technology, 2021, 36(3): 606−616.
[24] Wang D, Shang Y. A new active labeling method for deep learning. In: Proc. of the Int’l joint Conf. on Neural Networks (IJCNN).
Beijing: IEEE, 2014. 112−119.
[25] He T, Jin XM, Ding GG, et al. Towards better uncertainty sampling: Active learning with multiple views for deep convolutional
neural network. In: Proc. of the IEEE Int’l Conf. on Multimedia and Expo (ICME). Shanghai: IEEE, 2019. 1360−1365.
[26] Sener O, Savarese S. Active learning for convolutional neural networks: A core-set approach. In: Proc. of the Int’l Conf. on
Learning Representations (ICLR). Vancouver: Openreview.net, 2018.
[27] Long MS, Cao ZJ, Wang JM. Learning transferable features with deep adaptation networks. In: Proc. of the Int’l Conf. on Machine
Learning (ICML). Lille: JMLR.org, 2015. 97−105.
[28] Long MS, Zhu H, Wang JM, et al. Deep transfer learning with joint adaptation networks. In: Proc. of the Int’l Conf. on Machine
Learning (ICML). Sydney: PMLR, 2017. 2208−2217.
[29] Long MS, Cao ZJ, Wang JM, et al. Conditional adversarial domain adaptation. In: Advances in Neural Information Processing
Systems, Vol.31. Montreal, 2018.
[30] Ganin Y, Lempitsky V. Unsupervised domain adaptation by backpropagation. In: Proc. of the Int’l Conf. on Machine Learning
(ICML). Lille: JMLR.org, 2015. 1180−1189.
[31] Panareda Busto P, Gall J. Open set domain adaptation. In: Proc. of the IEEE Int’l Conf. on Computer Vision (ICCV). Venice: IEEE
Computer Society, 2017. 754−763.
[32] Saito K, Yamamoto S, Ushiku Y, et al. Open set domain adaptation by backpropagation. In: Proc. of the European Conf. on
Computer Vision (ECCV). Munich: Springer, 2018. 153−168.
[33] Busto PP, Iqbal A, Gall J. Open set domain adaptation for image and action recognition. IEEE Trans. on Pattern Analysis and
Machine Intelligence, 2018, 42(2): 413−429.
[34] Jiang P, Wu AM, Han YH, et al. Bidirectional adversarial training for semi-supervised domain adaptation. In: Proc. of the 29th Int’l
Conf. on Int’l Joint Conf. on Artificial Intelligence. ijcai.org, 2020. 934−940.
[35] Kim T, Kim C. Attract, perturb, and explore: Learning a feature alignment network for semi-supervised domain adaptation. In: Proc.
of the European Conf. on Computer Vision (ECCV). Glasgow: Springer, 2020. 591−607.
[36] Fu B, Cao ZJ, Wang JM, et al. Transferable query selection for active domain adaptation. In: Proc. of the IEEE/CVF Conf. on
Computer Vision and Pattern Recognition (CVPR). Virtual: IEEE Computer Society, 2021. 7272−7281.
[37] Xie BH, Yuan LH, Li SL, et al. Active learning for domain adaptation: An energy-based approach. In: Proc. of the AAAI Conf. on
Artificial Intelligence. Virtual: AAAI, 2022. 8708−8716.
[38] Fu B, Cao ZJ, Long MS. Learning to detect public classes for universal domain adaptation. In: Proc. of the European Conf. on
Computer Vision (ECCV). Glasgow: Springer, 2020. 567−583.
[39] Prabhu V, Chandrasekaran A, Saenko K, et al. Active domain adaptation via clustering uncertainty-weighted embeddings. In: Proc.
of the IEEE/CVF Int’l Conf. on Computer Vision (ICCV). Monteral: IEEE, 2021. 8505−8514.
[40] Ding YH, Sheng LJ, Liang J, et al. ProxyMix: Proxy-based mixup training with label refinery for source-free domain adaptation.
Neural Networks, 2023.
[41] Yang SQ, van de Weijer J, Herranz L, et al. Exploiting the intrinsic neighborhood structure for source-free domain adaptation. In:
Advances in Neural Information Processing Systems. Virtual, 2021. 29393−29405.
[42] Yang SQ, Wang YX, van de Weijer J, et al. Generalized source-free domain adaptation. In: Proc. of the IEEE/CVF Int’l Conf. on
Computer Vision (ICCV). Monteral: IEEE, 2021. 8978−8987.
[43] Liang J, Hu DP, Feng JS. Do we really need to access the source data? Source hypothesis transfer for unsupervised domain
adaptation. In: Proc. of the Int’l Conf. on Machine Learning (ICML). Virtual: PMLR, 2020. 6028−6039.
[44] Saenko K, Kulis B, Fritz M, et al. Adapting visual category models to new domains. In: Proc. of the European Conf. on Computer
Vision (ECCV). Grete: Springer, 2010. 213−226.
[45] Venkateswara H, Eusebio J, Chakraborty S, et al. Deep Hashing network for unsupervised domain adaptation. In: Proc. of the
IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE Computer Society, 2017. 5018−5027.