{"type":"ScholarlyArticle","title":"Disentangling Domain and General Representations for Time Series Classification","authors":[{"name":"Youmin Chen","slug":"youmin-chen","given":"Youmin","family":"Chen","affiliation":"Zhejiang University"},{"name":"Xinyu Yan","slug":"xinyu-yan","given":"Xinyu","family":"Yan","affiliation":"Zhejiang University"},{"name":"Yang Yang","slug":"yang-yang","given":"Yang","family":"Yang","affiliation":"Zhejiang University"},{"name":"Jianfeng Zhang","slug":"jianfeng-zhang","given":"Jianfeng","family":"Zhang","affiliation":"Huawei Noah's Ark Lab"},{"name":"Jing Zhang","slug":"jing-zhang","given":"Jing","family":"Zhang","affiliation":"Renmin University of China"},{"name":"Lujia Pan","slug":"lujia-pan","given":"Lujia","family":"Pan","affiliation":"Huawei Noah's Ark Lab"},{"name":"Juren Li","slug":"juren-li","given":"Juren","family":"Li","affiliation":"Zhejiang University"}],"abstract":"Modeling time series data has become a very at tractive research topic due to its wide application, such as human activity recognition, financial forecasting and sensor-based automatic system monitoring. Recently deep learning models have shown great advances in modeling the time series data but they heavily depend on a large amount of labeled data. To avoid costly labeling, this paper explores domain adaptation from a labeled source domain to the unlabeled target domain on time series data. To achieve the goal, we propose a disentangled representation learning framework named CADT to disentangle the domain-invariant features from the domain-specific ones. Particularly, CADT is injected with a novel class-wise hypersphere loss to improve the generalization of the classifier from the source domain to the target domain. Intuitively, it restricts the source data of the same class within the same hypersphere and minimizes the radius of it, which in turn enlarges the margin between different classes and makes the decision boundary of both domains easier. We further devise several kinds of domain-preserving data augmentation methods to better capture the domain-specific patterns. Extensive experiments on two public datasets and two real-world applications demonstrate the effectiveness of the proposed model against several state-of-the-art baselines.","doi":"10.24963/ijcai.2024/424","url":"https://ijcai.org/proceedings/2024/424","pdf":"https://ijcai.org/proceedings/2024/0424.pdf","markdown":"https://ijcai.org/proceedings/2024/424.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/424","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":424,"pages":{"first":3834,"last":3842},"keywords":["Machine Learning: ML: Classification","Machine Learning: ML: Deep learning architectures","Machine Learning: ML: Time series and data streams"],"videos":[],"references":[{"position":1,"raw":"[Arjovsky et al., 2017] Martín Arjovsky, Soumith Chintala, and Léon Bottou. Wasserstein generative adversarial net- works. In Proceedings of the 34th International Confer- ence on Machine Learning, ICML 2017, volume 70, pages 214-223, 2017.","title":"Martín Arjovsky, Soumith Chintala, and Léon Bottou. Wasserstein generative adversarial networks","authors":"Arjovsky","year":"2017","venue":"Proceedings of the 34th International Confer-ence on Machine Learning, ICML","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bai et al., 2018] Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. CoRR, abs/1803.01271, 2018.","title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","authors":"J Zico Shaojie Bai, Vladlen Kolter, Koltun","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bousmalis et al., 2017] Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, and Dilip Kr- ishnan. Unsupervised pixel-level domain adaptation with generative adversarial networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pages 95-104, 2017.","title":"Unsupervised pixel-level domain adaptation with generative adversarial networks","authors":"Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, Dilip Krishnan","year":"2017","venue":"IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cai et al., 2021] Ruichu Cai, Jiawei Chen, Zijian Li, Wei Chen, Keli Zhang, Junjian Ye, Zhuozhang Li, Xiaoyan Yang, and Zhenjie Zhang. Time series domain adapta- tion via sparse associative structure alignment. In Pro- ceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 6859-6867, 2021.","title":"Time series domain adaptation via sparse associative structure alignment","authors":"Ruichu Cai, Jiawei Chen, Zijian Li, Wei Chen, Keli Zhang, Junjian Ye, Zhuozhang Li, Xiaoyan Yang, Zhenjie Zhang","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2016] Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. In- fogan: Interpretable representation learning by informa- tion maximizing generative adversarial nets. In Advances in Neural Information Processing Systems 29, NIPS 2016, pages 2172-2180, 2016.","title":"Infogan: Interpretable representation learning by information maximizing generative adversarial nets","authors":"Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel","year":"2016","venue":"Advances in Neural Information Processing Systems 29, NIPS 2016","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Fulcher and Jones, 2014] Ben D. Fulcher and Nick S. Jones. Highly comparative feature-based time-series classifica- tion. IEEE Transactions on Knowledge and Data Engi- neering, 26(12):3026-3037, 2014.","title":"Highly comparative feature-based time-series classification","authors":"Ben D Fulcher, Nick S Jones","year":"2014","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Ganin and Lempitsky, 2015] Yaroslav Ganin and Victor S. Lempitsky. Unsupervised domain adaptation by backprop- agation. In Proceedings of the 32nd International Confer- ence on Machine Learning, ICML 2015, volume 37, pages 1180-1189, 2015.","title":"Unsupervised domain adaptation by backpropagation","authors":"Yaroslav Ganin, Victor S Lempitsky","year":"2015","venue":"Proceedings of the 32nd International Conference on Machine Learning, ICML 2015","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Glorot et al., 2011] Xavier Glorot, Antoine Bordes, and Yoshua Bengio. Domain adaptation for large-scale sen- timent classification: A deep learning approach. In Pro- ceedings of the 28th International Conference on Machine Learning, ICML 2011, pages 513-520, 2011.","title":"Domain adaptation for large-scale sentiment classification: A deep learning approach","authors":"Xavier Glorot, Antoine Bordes, Yoshua Bengio","year":"2011","venue":"Proceedings of the 28th International Conference on Machine Learning, ICML 2011","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Goodfellow et al., 2014] Ian J. Goodfellow, Jean Pouget- Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. Generative adversarial networks. CoRR, abs/1406.2661, 2014.","title":"Generative adversarial networks","authors":"Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C Courville, Yoshua Bengio","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hochreiter and Schmidhuber, 1997] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735-1780, 1997.","title":"Long short-term memory","authors":"Schmidhuber Hochreiter, Sepp Hochreiter, Jürgen Schmidhuber","year":"1997","venue":"Neural computation","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Jin et al., 2022] Xiaoyong Jin, Youngsuk Park, Danielle Maddix, Hao Wang, and Yuyang Wang. Domain adap- tation for time series forecasting via attention sharing. In International Conference on Machine Learning, pages 10280-10297. PMLR, 2022.","title":"Domain adaptation for time series forecasting via attention sharing","authors":"Xiaoyong Jin, Youngsuk Park, Danielle Maddix, Hao Wang, Yuyang Wang","year":"2022","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kim and Jeong, 2021] Mooseop Kim and Chi Yoon Jeong. Label-preserving data augmentation for mobile sensor data. Multidimensional Systems and Signal Processing, 32(1):115-129, 2021.","title":"Label-preserving data augmentation for mobile sensor data","authors":"Jeong ; Kim, Mooseop Kim, Chi Yoon, Jeong","year":"2021","venue":"Multidimensional Systems and Signal Processing","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Koltchinskii and Panchenko, 2002] Vladimir Koltchinskii and Dmitry Panchenko. Empirical margin distributions and bounding the generalization error of combined classifiers. The Annals of Statistics, 30(1):1-50, 2002.","title":"Vladimir Koltchinskii and Dmitry Panchenko. Empirical margin distributions and bounding the generalization error of combined classifiers","authors":"Panchenko Koltchinskii","year":"2002","venue":"The Annals of Statistics","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kwapisz et al., 2011] Jennifer R Kwapisz, Gary M Weiss, and Samuel A Moore. Activity recognition using cell phone accelerometers. ACM SigKDD Explorations Newsletter, 12(2):74-82, 2011.","title":"Activity recognition using cell phone accelerometers","authors":"Gary M Jennifer R Kwapisz, Samuel A Weiss, Moore","year":"2011","venue":"ACM SigKDD Explorations Newsletter","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Lee et al., 2019] Chen-Yu Lee, Tanmay Batra, Moham- mad Haris Baig, and Daniel Ulbricht. Sliced wasserstein discrepancy for unsupervised domain adaptation. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, pages 10285-10295, 2019.","title":"Sliced wasserstein discrepancy for unsupervised domain adaptation","authors":"Chen-Yu Lee, Tanmay Batra, Mohammad Haris Baig, Daniel Ulbricht","year":"2019","venue":"IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2021] Jingjing Li, Mengmeng Jing, Hongzu Su, Ke Lu, Lei Zhu, and Heng Tao Shen. Faster domain adap- tation networks. IEEE Transactions on Knowledge and Data Engineering, pages 1-1, 2021.","title":"Faster domain adaptation networks","authors":"Jingjing Li, Mengmeng Jing, Hongzu Su, Ke Lu, Lei Zhu, Heng Tao Shen","year":"2021","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Li et al., 2022] Keqiuyin Li, Jie Lu, Hua Zuo, and Guangquan Zhang. Dynamic classifier alignment for un- supervised multi-source domain adaptation. IEEE Trans- actions on Knowledge and Data Engineering, pages 1-1, 2022.","title":"Dynamic classifier alignment for unsupervised multi-source domain adaptation","authors":"Li","year":"2022","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lin and Jung, 2017] Yuan-Pin Lin and Tzyy-Ping Jung. Im- proving eeg-based emotion classification using condi- tional transfer learning. Frontiers in human neuroscience, 11:334, 2017.","title":"Yuan-Pin Lin and Tzyy-Ping Jung. Improving eeg-based emotion classification using conditional transfer learning","authors":"Jung Lin","year":"2017","venue":"Frontiers in human neuroscience","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Liu et al., 2019] Hongfu Liu, Ming Shao, Zhengming Ding, and Yun Fu. Structure-preserved unsupervised domain adaptation. IEEE Transactions on Knowledge and Data Engineering, 31(4):799-812, 2019.","title":"Structure-preserved unsupervised domain adaptation","authors":"Hongfu Liu, Ming Shao, Zhengming Ding, Yun Fu","year":"2019","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Liu et al., 2022] Shenghua Liu, Bin Zhou, Quan Ding, Bryan Hooi, Zheng bo Zhang, Huawei Shen, and Xueqi Cheng. Time series anomaly detection with adversarial re- construction networks. IEEE Transactions on Knowledge and Data Engineering, pages 1-1, 2022.","title":"Time series anomaly detection with adversarial reconstruction networks","authors":"Shenghua Liu, Bin Zhou, Quan Ding, Bryan Hooi, Huawei Zheng Bo Zhang, Xueqi Shen, Cheng","year":"2022","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Long et al., 2015] Mingsheng Long, Jianmin Wang, Ji- aguang Sun, and Philip S. Yu. Domain invariant trans- fer kernel learning. IEEE Transactions on Knowledge and Data Engineering, 27(6):1519-1532, 2015.","title":"Domain invariant transfer kernel learning","authors":"Mingsheng Long, Jianmin Wang, Jiaguang Sun, Philip S Yu","year":"2015","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Long et al., 2016] Mingsheng Long, Jianmin Wang, Yue Cao, Jiaguang Sun, and Philip S. Yu. Deep learning of transferable representation for scalable domain adaptation. IEEE Transactions on Knowledge and Data Engineering, 28(8):2027-2040, 2016.","title":"Deep learning of transferable representation for scalable domain adaptation","authors":"Mingsheng Long, Jianmin Wang, Yue Cao, Jiaguang Sun, Philip S Yu","year":"2016","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Mathieu et al., 2016] Michaël Mathieu, Junbo Jake Zhao, Pablo Sprechmann, Aditya Ramesh, and Yann LeCun. Disentangling factors of variation in deep representation using adversarial training. In Advances in Neural Informa- tion Processing Systems 29, NIPS 2016, pages 5041-5049, 2016.","title":"Disentangling factors of variation in deep representation using adversarial training","authors":"Michaël Mathieu, Jake Junbo, Pablo Zhao, Aditya Sprechmann, Yann Ramesh, Lecun","year":"2016","venue":"Advances in Neural Information Processing Systems 29, NIPS 2016","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Ozyurt et al., 2023] Yilmazcan Ozyurt, Stefan Feuerriegel, and Ce Zhang. Contrastive learning for unsupervised do- main adaptation of time series. ICLR, 2023.","title":"Contrastive learning for unsupervised domain adaptation of time series","authors":"Ozyurt","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Pan and Yang, 2010] Sinno Jialin Pan and Qiang Yang. A survey on transfer learning. IEEE Transactions on Knowl- edge and Data Engineering, 22(10):1345-1359, 2010.","title":"A survey on transfer learning","authors":"Jialin Sinno, Qiang Pan, Yang","year":"2010","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Patel et al., 2015] Vishal M. Patel, Raghuraman Gopalan, Ruonan Li, and Rama Chellappa. Visual domain adapta- tion: A survey of recent advances. IEEE Signal Processing Magazine, 32(3):53-69, 2015.","title":"Visual domain adaptation: A survey of recent advances","authors":"M Vishal, Raghuraman Patel, Ruonan Gopalan, Rama Li, Chellappa","year":"2015","venue":"IEEE Signal Processing Magazine","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Purushotham et al., 2017] Sanjay Purushotham, Wilka Car- valho, Tanachat Nilanon, and Yan Liu. Variational re- current adversarial deep domain adaptation. In 5th Inter- national Conference on Learning Representations, ICLR 2017, 2017.","title":"Variational recurrent adversarial deep domain adaptation","authors":"Sanjay Purushotham, Wilka Carvalho, Tanachat Nilanon, Yan Liu","year":"2017","venue":"5th International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Qian et al., 2019] Hangwei Qian, Sinno Jialin Pan, Bing- shui Da, and Chunyan Miao. A novel distribution- embedded neural network for sensor-based activity recog- nition. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, pages 5614-5620, 2019.","title":"A novel distributionembedded neural network for sensor-based activity recognition","authors":"Qian","year":"2019","venue":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Qiang et al., 2021] Wenwen Qiang, Jiangmeng Li, Chang- wen Zheng, Bing Su, and Hui Xiong. Robust local pre- serving and global aligning network for adversarial do- main adaptation. IEEE Transactions on Knowledge and Data Engineering, pages 1-1, 2021.","title":"Robust local preserving and global aligning network for adversarial domain adaptation","authors":"Wenwen Qiang, Jiangmeng Li, Changwen Zheng, Bing Su, Hui Xiong","year":"2021","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Ragab and Eldele, 2022] Mohamed Ragab and Emadeldeen Eldele. Subject-wise Sleep Stage Data, 2022.","title":"Subject-wise Sleep Stage Data","authors":"Mohamed Ragab, Emadeldeen Eldele","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Reyes-Ortiz et al., 2016] Jorge-L Reyes-Ortiz, Luca Oneto, Albert Samà, Xavier Parra, and Davide Anguita. Transition-aware human activity recognition using smart- phones. Neurocomputing, 171:754-767, 2016.","title":"Transition-aware human activity recognition using smartphones","authors":"Jorge-L Reyes-Ortiz, Luca Oneto, Albert Samà, Xavier Parra, Davide Anguita","year":"2016","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Sun and Saenko, 2016] Baochen Sun and Kate Saenko. Deep coral: Correlation alignment for deep domain adap- tation. In Gang Hua and Hervé Jégou, editors, Computer Vision -ECCV 2016 Workshops, pages 443-450, Cham, 2016. Springer International Publishing.","title":"Deep coral: Correlation alignment for deep domain adaptation","authors":"Saenko ; Sun, Baochen Sun, Kate Saenko","year":"2016","venue":"Computer Vision -ECCV 2016 Workshops","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Sun et al., 2015] Shiliang Sun, Honglei Shi, and Yuanbin Wu. A survey of multi-source domain adaptation. Infor- mation Fusion, 24:84-92, 2015.","title":"A survey of multi-source domain adaptation","authors":"Shiliang Sun, Honglei Shi, Yuanbin Wu","year":"2015","venue":"Information Fusion","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Tan et al., 2022] Chang Wei Tan, Angus Dempster, Christoph Bergmeir, and Geoffrey I Webb. Multirocket: multiple pooling operators and transformations for fast and effective time series classification. Data Mining and Knowledge Discovery, 36(5):1623-1646, 2022.","title":"Multirocket: multiple pooling operators and transformations for fast and effective time series classification","authors":"Chang Wei Tan, Angus Dempster, Christoph Bergmeir, Geoffrey I Webb","year":"2022","venue":"Data Mining and Knowledge Discovery","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Tonutti et al., 2019] Michele Tonutti, Emanuele Ruffaldi, Alessandro Cattaneo, and Carlo Alberto Avizzano. Ro- bust and subject-independent driving manoeuvre antici- pation through domain-adversarial recurrent neural net- works. Robotics Auton. Syst., 115:162-173, 2019.","title":"Robust and subject-independent driving manoeuvre anticipation through domain-adversarial recurrent neural networks","authors":"Michele Tonutti, Emanuele Ruffaldi, Alessandro Cattaneo, Carlo Alberto Avizzano","year":"2019","venue":"Robotics Auton. Syst","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Tran et al., 2017] Luan Tran, Xi Yin, and Xiaoming Liu. Disentangled representation learning GAN for pose- invariant face recognition. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pages 1283-1292, 2017.","title":"Disentangled representation learning GAN for poseinvariant face recognition","authors":"Luan Tran, Xi Yin, Xiaoming Liu","year":"2017","venue":"IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Tzeng et al., 2017] Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. Adversarial discriminative domain adaptation. In 2017 IEEE Conference on Com- puter Vision and Pattern Recognition, pages 2962-2971, 2017.","title":"Adversarial discriminative domain adaptation","authors":"Tzeng","year":"2017","venue":"2017 IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wang et al., 2018] Fang Wang, Sheng-hua Zhong, Jianfeng Peng, Jianmin Jiang, and Yan Liu. Data augmentation for eeg-based emotion recognition with deep convolu- tional neural networks. In MultiMedia Modeling: 24th In- ternational Conference, MMM 2018, Bangkok, Thailand, February 5-7, 2018, Proceedings, Part II 24, pages 82-93. Springer, 2018.","title":"Data augmentation for eeg-based emotion recognition with deep convolutional neural networks","authors":"Fang Wang, Sheng-Hua Zhong, Jianfeng Peng, Jianmin Jiang, Yan Liu","year":"2018","venue":"MultiMedia Modeling: 24th International Conference, MMM 2018","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Wilson et al., 2020] Garrett Wilson, Janardhan Rao Doppa, and Diane J. Cook. Multi-source deep domain adaptation with weak supervision for time-series sensor data. In The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '2020, pages 1768-1778, 2020.","title":"Multi-source deep domain adaptation with weak supervision for time-series sensor data","authors":"Garrett Wilson, Janardhan Rao Doppa, Diane J Cook","year":"2020","venue":"The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '2020","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Yan et al., 2021] Yuguang Yan, Hanrui Wu, Yuzhong Ye, Chaoyang Bi, Min Lu, Dapeng Liu, Qingyao Wu, and Michael Kwok-Po Ng. Transferable feature selection for unsupervised domain adaptation. IEEE Transactions on Knowledge and Data Engineering, pages 1-1, 2021.","title":"Transferable feature selection for unsupervised domain adaptation","authors":"Yuguang Yan, Hanrui Wu, Yuzhong Ye, Chaoyang Bi, Min Lu, Dapeng Liu, Qingyao Wu, Michael Kwok, -Po Ng","year":"2021","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Yang et al., 2021] Shuai Yang, Kui Yu, Fuyuan Cao, Lin Liu, Hao Wang, and Jiuyong Li. Learning causal represen- tations for robust domain adaptation. IEEE Transactions on Knowledge and Data Engineering, pages 1-1, 2021.","title":"Learning causal representations for robust domain adaptation","authors":"Shuai Yang, Kui Yu, Fuyuan Cao, Lin Liu, Hao Wang, Jiuyong Li","year":"2021","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zellinger et al., 2017] Werner Zellinger, Thomas Grub- inger, Edwin Lughofer, Thomas Natschläger, and Susanne Saminger-Platz. Central moment discrepancy (CMD) for domain-invariant representation learning. In 5th Inter- national Conference on Learning Representations, ICLR 2017, 2017.","title":"Central moment discrepancy (CMD) for domain-invariant representation learning","authors":"Werner Zellinger, Thomas Grubinger, Edwin Lughofer, Thomas Natschläger, Susanne Saminger-Platz","year":"2017","venue":"5th International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhang et al., 2021] Qian Zhang, Wenhui Liao, Guangquan Zhang, Bo Yuan, and Jie Lu. A deep dual adversarial network for cross-domain recommendation. IEEE Trans- actions on Knowledge and Data Engineering, pages 1-1, 2021.","title":"A deep dual adversarial network for cross-domain recommendation","authors":"Qian Zhang, Wenhui Liao, Guangquan Zhang, Bo Yuan, Jie Lu","year":"2021","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhuang et al., 2019] Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He. A comprehensive survey on trans- fer learning. CoRR, abs/1911.02685, 2019.","title":"A comprehensive survey on transfer learning","authors":"Zhuang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}