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李梓健 等: 基于隐变量解耦学习的时间序列领域自适应方法                                                    5569


                 [45]   Zhu YC, Zhuang FZ, Wang JD, Ke GL, Chen JW, Bian J, Xiong H, He Q. Deep subdomain adaptation network for image classification.
                     IEEE Trans. on Neural Networks and Learning Systems, 2021, 32(4): 1713–1722. [doi: 10.1109/TNNLS.2020.2988928]
                 [46]   Long  MS,  Cao  ZJ,  Wang  JM,  Jordan  MI.  Conditional  adversarial  domain  adaptation.  In:  Proc.  of  the  32nd  Int’l  Conf.  on  neural
                     Information Processing Systems. Montréal: Curran Associates Inc., 2018. 1647–1657.
                 [47]   Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans. on Knowledge and Data Engineering, 2010, 22(10): 1345–1359. [doi: 10.
                     1109/TKDE.2009.191]
                 [48]   Sakai H. On the spectral density matrix of a periodic ARMA process. Journal of Time Series Analysis, 1991, 12(1): 73–82. [doi: 10.1111/
                     j.1467-9892.1991.tb00069.x]
                 [49]   Liu GS, Zheng Y, Xie XR, Huang L, Ding HL. A dual active domain adaptation algorithm based on loss prediction strategy. Chinese
                     Journal of Computers, 2023, 46(3): 579–593 (in Chinese with English abstract). [doi: 10.11897/SP.J.1016.2023.00579]
                 [50]   Tang JK, Zhang H, Zhang ZQ, Wu TY. Image classification for unsupervised domain adaptation based on task relevant feature separation
                     network. Computer Science, 2023, 50(11A): 230100068 (in Chinese with English abstract). [doi: 10.11896/jsjkx.230100068]
                 [51]   Anguita D, Ghio A, Oneto L, Parra X, Reyes-Ortiz JL. A public domain dataset for human activity recognition using smartphones. In:
                     Proc. of the 2013 European Symp. on Artificial Neural Networks, 2013. 437–442.
                 [52]   Zhang K, Schölkopf B, Muandet K, Wang ZK. Domain adaptation under target and conditional shift. In: Proc. of the 30th Int’l Conf. on
                     Machine Learning. Atlanta: JMLR, 2013. III-819–III-827.
                 [53]   Zhao H, Melibari M, Poupart P. On the relationship between sum-product networks and Bayesian networks. In: Proc. of the 32nd Int’l
                     Conf. on Machine Learning. Lille: JMLR, 2015. 116–124.
                 [54]   Ganin Y, Lempitsky V. Unsupervised domain adaptation by backpropagation. In: Proc. of the 32nd Int’l Conf. on Machine Learning.
                     Lille: JMLR, 2015. 1180–1189.
                 [55]   Zhao H, Zhang SH, Wu GH, Costeira JP, Moura JMF, Gordon GJ. Adversarial multiple source domain adaptation. In: Proc. of the 32nd
                     Int’l Conf. on Neural Information Processing Systems. Montréal: Curran Associates Inc., 2018. 8568–8579.
                 [56]   Alam F, Joty S, Imran M. Domain adaptation with adversarial training and graph embeddings. In: Proc. of the 56th Annual Meeting of the
                     Association for Computational Linguistics (Vol. 1: Long Papers). Melbourne: Association for Computational Linguistics, 2018. 1077–
                     1087. [doi: 10.18653/v1/P18-1099]
                 [57]   Wright D, Augenstein I. Transformer based multi-source domain adaptation. In: Proc. of the 2020 Conf. on Empirical Methods in Natural
                     Language Processing (EMNLP). Association for Computational Linguistics, 2020. 7963–7974. [doi: 10.18653/v1/2020.emnlp-main.639]
                 [58]   Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9(8): 1735–1780. [doi: 10.1162/neco.1997.9.8.1735]
                 [59]   Chen XY, Wang SN, Wang JM, Long MS. Representation subspace distance for domain adaptation regression. In: Proc. of the 38th Int’l
                     Conf. on Machine Learning. PMLR, 2021. 1749–1759.
                 [60]   Ragab  M,  Eldele  E,  Chen  ZH,  Wu  M,  Kwoh  CK,  Li  XL.  Self-supervised  autoregressive  domain  adaptation  for  time  series  data.
                     arXiv:2111.14834, 2021.
                 [61]   Kong LJ, Xie SA, Yao WR, Zheng YJ, Chen GY, Stojanov P, Akinwande V, Zhang K. Partial disentanglement for domain adaptation. In:
                     Proc. of the 39th Int’l Conf. on Machine Learning. Baltimore: PMLR, 2022. 11455–11472.
                 [62]   Lipton Z, Wang YX, Smola A. Detecting and correcting for label shift with black box predictors. In: Proc. of the 35th Int’l Conf. on
                     Machine Learning. Stockholm: PMLR. 3122–3130.
                 [63]   Graves A. Long short-term memory. Supervised Sequence Labelling with Recurrent Neural Networks. Springer, 2012. 37–45. [doi: 10.
                     1007/978-3-642-24797-2_4]
                 [64]   Sun  BC,  Feng  JS,  Saenko  K.  Correlation  alignment  for  unsupervised  domain  adaptation.  In:  Csurka  G,  ed.  Domain  Adaptation  in
                     Computer Vision Applications. Cham: Springer, 2017. 153–171. [doi: 10.1007/978-3-319-58347-1_8]
                 [65]   Jin Y, Wang XM, Long MS, Wang JM. Minimum class confusion for versatile domain adaptation. In: Proc. of the 16th European Conf.
                     on Computer Vision. Glasgow: Springer. 2020. [doi: 10.1007/978-3-030-58589-1_28]
                 [66]   Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I. Attention is all you need. arXiv:1706.03762,
                     2023.
                 [67]   Vapnik VN. The Nature of Statistical Learning Theory. 2nd ed., New York: Springer, 2000. [doi: 10.1007/978-1-4757-3264-1]
                 [68]   Stacke K, Eilertsen G, Unger J, Lundstrom C. Measuring domain shift for deep learning in histopathology. IEEE Journal of Biomedical
                     and Health Informatics, 2021, 25(2): 325–336. [doi: 10.1109/jbhi.2020.3032060]
                 [69]   Peharz R, Tschiatschek S, Pernkopf F, Domingos P. On theoretical properties of sum-product networks. In: Proc. of the 18th Int’l Conf.
                     on Artificial Intelligence and Statistics. San Diego: JMLR, 2015. 744–752.
                 [70]   Gens R, Domingos P. Learning the structure of sum-product networks. In: Proc. of the 30th Int’l Conf. on Machine Learning. Atlanta:
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