{"type":"ScholarlyArticle","title":"More is Better: Deep Domain Adaptation with Multiple Sources","authors":[{"name":"Sicheng Zhao","slug":"sicheng-zhao","given":"Sicheng","family":"Zhao","affiliation":"BNRist , Tsinghua University , China"},{"name":"Hui Chen","slug":"hui-chen","given":"Hui","family":"Chen","affiliation":"BNRist , Tsinghua University , China"},{"name":"Hu Huang","slug":"hu-huang","given":"Hu","family":"Huang","affiliation":"Peking University Shenzhen Graduate School , China"},{"name":"Pengfei Xu","slug":"pengfei-xu","given":"Pengfei","family":"Xu","affiliation":"Didi Chuxing , China"},{"name":"Guiguang Ding","slug":"guiguang-ding","given":"Guiguang","family":"Ding","affiliation":"BNRist , Tsinghua University , China; School of Software , Tsinghua University , China"}],"abstract":"In many practical applications, it is often difficult and expensive to obtain large-scale labeled data to train state-of-the-art deep neural networks. Therefore, transferring the learned knowledge from a separate, labeled source domain to an unlabeled or sparsely labeled target domain becomes an appealing alternative. However, direct transfer often results in significant performance decay due to domain shift. Domain adaptation (DA) aims to address this problem by aligning the distributions between the source and target domains. Multi-source domain adaptation (MDA) is a powerful and practical extension in which the labeled data may be collected from multiple sources with different distributions. In this survey, we first define various MDA strategies. Then we systematically summarize and compare modern MDA methods in the deep learning era from different perspectives, followed by commonly used datasets and a brief benchmark. Finally, we discuss future research directions for MDA that are worth investigating.","doi":"10.24963/ijcai.2024/923","url":"https://ijcai.org/proceedings/2024/923","pdf":"https://ijcai.org/proceedings/2024/0923.pdf","markdown":"https://ijcai.org/proceedings/2024/923.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/923","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":"Survey Track","track":"Survey Track","number":923,"pages":{"first":8354,"last":8362},"keywords":["Machine Learning: ML: Multi-task and transfer learning","Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning"],"videos":[],"references":[{"position":1,"raw":"[Ahmed et al., 2021] Sk Miraj Ahmed, Dripta S Raychaud- huri, et al. Unsupervised multi-source domain adaptation without access to source data. In CVPR, 2021.","title":"Sk Miraj Ahmed, Dripta S Raychaudhuri, et al. Unsupervised multi-source domain adaptation without access to source data","authors":"Ahmed","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Belal et al., 2024] Atif Belal, Akhil Meethal, et al. Multi- source domain adaptation for object detection with prototype-based mean teacher. In WACV, 2024.","title":"Multisource domain adaptation for object detection with prototype-based mean teacher","authors":"Atif Belal, Akhil Meethal","year":"2024","venue":"WACV","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Ben-David et al., 2010] Shai Ben-David, John Blitzer, et al. A theory of learning from different domains. ML, 2010.","title":"A theory of learning from different domains","authors":"Shai Ben-David, John Blitzer","year":"2010","venue":"ML","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Blitzer et al., 2008] John Blitzer, Koby Crammer, et al. Learning bounds for domain adaptation. In NIPS, 2008.","title":"Learning bounds for domain adaptation","authors":"John Blitzer, Koby Crammer","year":"2008","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cai et al., 2023] Yuxiang Cai, Meng Xi, Yongheng Shang, and Jianwei Yin. Exploring high-correlation source do- main information for multi-source domain adaptation in semantic segmentation. In ACM MM, 2023.","title":"Exploring high-correlation source domain information for multi-source domain adaptation in semantic segmentation","authors":"Yuxiang Cai, Meng Xi, Yongheng Shang, Jianwei Yin","year":"2023","venue":"ACM MM","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chen and Marchand, 2023] Qi Chen and Mario Marchand. Algorithm-dependent bounds for representation learning of multi-source domain adaptation. In AISTATS, 2023.","title":"Algorithm-dependent bounds for representation learning of multi-source domain adaptation","authors":"Chen, Marchand ; ] Qi Chen, Mario Marchand","year":"2023","venue":"AISTATS","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Dong et al., 2021] Jiahua Dong, Zhen Fang, Anjin Liu, Gan Sun, and Tongliang Liu. Confident anchor-induced multi- source free domain adaptation. In NeurIPS, 2021.","title":"Confident anchor-induced multisource free domain adaptation","authors":"Jiahua Dong, Zhen Fang, Anjin Liu, Gan Sun, Tongliang Liu","year":"2021","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Feng et al., 2021] Haozhe Feng, Zhaoyang You, et al. Kd3a: Unsupervised multi-source decentralized domain adaptation via knowledge distillation. In ICML, 2021.","title":"Kd3a: Unsupervised multi-source decentralized domain adaptation via knowledge distillation","authors":"Feng","year":"2021","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Fu et al., 2021] Yangye Fu, Ming Zhang, et al. Partial feature selection and alignment for multi-source domain adaptation. In CVPR, 2021.","title":"Partial feature selection and alignment for multi-source domain adaptation","authors":"Fu","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Ganin et al., 2016] Yaroslav Ganin, Evgeniya Ustinova, et al. Domain-adversarial training of neural networks. JMLR, 2016.","title":"Domain-adversarial training of neural networks","authors":"Yaroslav Ganin, Evgeniya Ustinova","year":"2016","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Gong et al., 2021] Rui Gong, Dengxin Dai, et al. mdalu: Multi-source domain adaptation and label unification with partial datasets. In ICCV, 2021.","title":"mdalu: Multi-source domain adaptation and label unification with partial datasets","authors":"Rui Gong, Dengxin Dai","year":"2021","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Guo et al., 2018] Jiang Guo, Darsh Shah, and Regina Barzi- lay. Multi-source domain adaptation with mixture of ex- perts. In EMNLP, 2018.","title":"Multi-source domain adaptation with mixture of experts","authors":"Jiang Guo, Darsh Shah, Regina Barzilay","year":"2018","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Guo et al., 2020] Han Guo, Ramakanth Pasunuru, and Mo- hit Bansal. Multi-source domain adaptation for text clas- sification via distancenet-bandits. In AAAI, 2020.","title":"Multi-source domain adaptation for text classification via distancenet-bandits","authors":"Han Guo, Ramakanth Pasunuru, Mohit Bansal","year":"2020","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Han et al., 2023] Zhongyi Han, Zhiyan Zhang, et al. Dis- criminability and transferability estimation: a bayesian source importance estimation approach for multi-source- free domain adaptation. In AAAI, 2023.","title":"Discriminability and transferability estimation: a bayesian source importance estimation approach for multi-sourcefree domain adaptation","authors":"Zhongyi Han, Zhiyan Zhang","year":"2023","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[He et al., 2021] Jianzhong He, Xu Jia, et al. Multi-source domain adaptation with collaborative learning for seman- tic segmentation. In CVPR, 2021.","title":"Multi-source domain adaptation with collaborative learning for semantic segmentation","authors":"Jianzhong He, Xu Jia","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Hoffman et al., 2018] Judy Hoffman, Mehryar Mohri, and Ningshan Zhang. Algorithms and theory for multiple- source adaptation. In NIPS, 2018.","title":"Algorithms and theory for multiplesource adaptation","authors":"Judy Hoffman, Mehryar Mohri, Ningshan Zhang","year":"2018","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kang et al., 2022] Guoliang Kang, Lu Jiang, et al. Con- trastive adaptation network for single-and multi-source do- main adaptation. IEEE TPAMI, 2022.","title":"Contrastive adaptation network for single-and multi-source domain adaptation","authors":"Guoliang Kang, Lu Jiang","year":"2022","venue":"IEEE TPAMI","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Kang et al., 2023] Myeongkyun Kang, Philip Chikontwe, et al. Structure-preserving image translation for multi- source medical image domain adaptation. PR, 2023.","title":"Structure-preserving image translation for multisource medical image domain adaptation","authors":"Kang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Karisani, 2022] Payam Karisani. Multiple-source domain adaptation via coordinated domain encoders and paired classifiers. In AAAI, 2022.","title":"Multiple-source domain adaptation via coordinated domain encoders and paired classifiers","authors":"Payam Karisani","year":"2022","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Kundu et al., 2022] Jogendra Nath Kundu, Suvaansh Bhambri, et al. Concurrent subsidiary supervision for unsupervised source-free domain adaptation. In ECCV, 2022.","title":"Concurrent subsidiary supervision for unsupervised source-free domain adaptation","authors":"Kundu","year":"2022","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Li and Hospedales, 2020] Da Li and Timothy Hospedales. Online meta-learning for multi-source and semi- supervised domain adaptation. In ECCV, 2020.","title":"Da Li and Timothy Hospedales. Online meta-learning for multi-source and semisupervised domain adaptation","authors":"Hospedales Li","year":"2020","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Li et al., 2018a] Yanghao Li, Naiyan Wang, Jianping Shi, Xiaodi Hou, and Jiaying Liu. Adaptive batch normaliza- tion for practical domain adaptation. PR, 2018.","title":"Adaptive batch normalization for practical domain adaptation","authors":"Li","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Li et al., 2018b] Yitong Li, Michael Andrew Murias, et al. Extracting relationships by multi-domain matching. In NIPS, 2018.","title":"Extracting relationships by multi-domain matching","authors":"Li","year":"2018","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Li et al., 2021a] Ruihuang Li, Xu Jia, et al. T-svdnet: Exploring high-order prototypical correlations for multi- source domain adaptation. In ICCV, 2021.","title":"T-svdnet: Exploring high-order prototypical correlations for multisource domain adaptation","authors":"Li","year":"2021","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Li et al., 2021b] Yunsheng Li, Lu Yuan, Yinpeng Chen, Pei Wang, and Nuno Vasconcelos. Dynamic transfer for multi- source domain adaptation. In CVPR, 2021.","title":"Dynamic transfer for multisource domain adaptation","authors":"Li","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Li et al., 2022] Keqiuyin Li, Jie Lu, Hua Zuo, and Guangquan Zhang. Multi-source contribution learning for domain adaptation. IEEE TNNLS, 2022.","title":"Multi-source contribution learning for domain adaptation","authors":"Li","year":"2022","venue":"IEEE TNNLS","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Li et al., 2023a] Keqiuyin Li, Jie Lu, et al. Dynamic classi- fier alignment for unsupervised multi-source domain adap- tation. IEEE TKDE, 2023.","title":"Dynamic classifier alignment for unsupervised multi-source domain adaptation","authors":"Li","year":"2023","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Li et al., 2023b] Zijian Li, Ruichu Cai, et al. Subspace iden- tification for multi-source domain adaptation. In NeurIPS, 2023.","title":"Subspace identification for multi-source domain adaptation","authors":"Li","year":"2023","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Liang et al., 2022] Jian Liang, Dapeng Hu, Jiashi Feng, and Ran He. Dine: Domain adaptation from single and multi- ple black-box predictors. In CVPR, 2022.","title":"Dine: Domain adaptation from single and multiple black-box predictors","authors":"Jian Liang, Dapeng Hu, Jiashi Feng, Ran He","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Lin et al., 2020] Chuang Lin, Sicheng Zhao, Lei Meng, and Tat-Seng Chua. Multi-source domain adaptation for visual sentiment classification. In AAAI, 2020.","title":"Multi-source domain adaptation for visual sentiment classification","authors":"Chuang Lin, Sicheng Zhao, Lei Meng, Tat-Seng Chua","year":"2020","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Liu and Ren, 2022] Yong-Hui Liu and Chuan-Xian Ren. A two-way alignment approach for unsupervised multi- source domain adaptation. PR, 2022.","title":"A two-way alignment approach for unsupervised multisource domain adaptation","authors":"Yong-Hui Liu, Chuan-Xian Ren","year":"2022","venue":"PR","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Luo et al., 2022] Zhipeng Luo, Xiaobing Zhang, et al. Do- main consistency regularization for unsupervised multi- source domain adaptive classification. PR, 2022.","title":"Domain consistency regularization for unsupervised multisource domain adaptive classification","authors":"Zhipeng Luo, Xiaobing Zhang","year":"2022","venue":"PR","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Mansour et al., 2009] Yishay Mansour, Mehryar Mohri, and Afshin. Rostamizadeh. Domain adaptation with mul- tiple sources. In NIPS, 2009.","title":"Rostamizadeh. Domain adaptation with multiple sources","authors":"Yishay Mansour, Mehryar Mohri, Afshin","year":"2009","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Nguyen et al., 2021] Van-Anh Nguyen, Tuan Nguyen, et al. Stem: An approach to multi-source domain adaptation with guarantees. In ICCV, 2021.","title":"Stem: An approach to multi-source domain adaptation with guarantees","authors":"Van-Anh, Tuan Nguyen, Nguyen","year":"2021","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Park and Lee, 2021] Geon Yeong Park and Sang Wan Lee. Information-theoretic regularization for multi-source do- main adaptation. In ICCV, 2021.","title":"Information-theoretic regularization for multi-source domain adaptation","authors":"Lee ; Park, Yeong Geon, Sang Wan Park, Lee","year":"2021","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Peng et al., 2019] Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang. Moment match- ing for multi-source domain adaptation. In ICCV, 2019.","title":"Moment matching for multi-source domain adaptation","authors":"Peng","year":"2019","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Peng et al., 2020] Xingchao Peng, Zijun Huang, Yizhe Zhu, and Kate Saenko. Federated adversarial domain adapta- tion. In ICLR, 2020.","title":"Federated adversarial domain adaptation","authors":"Peng","year":"2020","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Rakshit et al., 2020] Sayan Rakshit, Dipesh Tamboli, et al. Multi-source open-set deep adversarial domain adaptation. In ECCV, 2020.","title":"Multi-source open-set deep adversarial domain adaptation","authors":"Sayan Rakshit, Dipesh Tamboli","year":"2020","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Redko et al., 2019] Ievgen Redko, Nicolas Courty, et al. Optimal transport for multi-source domain adaptation un- der target shift. In AISTATS, 2019.","title":"Optimal transport for multi-source domain adaptation under target shift","authors":"Redko","year":"2019","venue":"AISTATS","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Ren et al., 2022] Chuan-Xian Ren, Yong-Hui Liu, et al. Multi-source unsupervised domain adaptation via pseudo target domain. IEEE TIP, 2022.","title":"Multi-source unsupervised domain adaptation via pseudo target domain","authors":"Yong-Hui Chuan-Xian Ren, Liu","year":"2022","venue":"IEEE TIP","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Shen et al., 2023] Maohao Shen, Yuheng Bu, and Gre- gory W Wornell. On balancing bias and variance in unsu- pervised multi-source-free domain adaptation. In ICML, 2023.","title":"On balancing bias and variance in unsupervised multi-source-free domain adaptation","authors":"Shen","year":"2023","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Shui et al., 2021] Changjian Shui, Zijian Li, Jiaqi Li, Chris- tian Gagné, Charles X Ling, and Boyu Wang. Aggregating from multiple target-shifted sources. In ICML, 2021.","title":"Aggregating from multiple target-shifted sources","authors":"Changjian Shui, Zijian Li, Jiaqi Li, Christian Gagné, Charles X Ling, Boyu Wang","year":"2021","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Sun et al., 2015] Shiliang Sun, Honglei Shi, and Yuanbin Wu. A survey of multi-source domain adaptation. INF- FUS, 2015.","title":"A survey of multi-source domain adaptation","authors":"Shiliang Sun, Honglei Shi, Yuanbin Wu","year":"2015","venue":"INF-FUS","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Venkat et al., 2020] Naveen Venkat, Jogendra Nath Kundu, et al. Your classifier can secretly suffice multi-source do- main adaptation. In NeurIPS, 2020.","title":"Your classifier can secretly suffice multi-source domain adaptation","authors":"Naveen Venkat, Jogendra Nath Kundu","year":"2020","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Wang et al., 2020] Hang Wang, Minghao Xu, et al. Learn- ing to combine: Knowledge aggregation for multi-source domain adaptation. In ECCV, 2020.","title":"Learning to combine: Knowledge aggregation for multi-source domain adaptation","authors":"Wang","year":"2020","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Wang et al., 2022] Zengmao Wang, Chaoyang Zhou, Bo Du, and Fengxiang He. Self-paced supervision for multi-source domain adaptation. In IJCAI, 2022.","title":"Self-paced supervision for multi-source domain adaptation","authors":"Wang","year":"2022","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2022/493"},{"position":47,"raw":"[Wen et al., 2020] Junfeng Wen, Russell Greiner, and Dale Schuurmans. Domain aggregation networks for multi- source domain adaptation. In ICML, 2020.","title":"Domain aggregation networks for multisource domain adaptation","authors":"Junfeng Wen, Russell Greiner, Dale Schuurmans","year":"2020","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Wilson and Cook, 2020] Garrett Wilson and Diane J Cook. A survey of unsupervised deep domain adaptation. ACM TIST, 2020.","title":"A survey of unsupervised deep domain adaptation","authors":"Garrett Wilson, Diane J Cook","year":"2020","venue":"ACM TIST","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Wu and Shi, 2022] Hui Wu and Xiaodong Shi. Adversarial soft prompt tuning for cross-domain sentiment analysis. In ACL, 2022.","title":"Adversarial soft prompt tuning for cross-domain sentiment analysis","authors":"Hui Wu, Xiaodong Shi","year":"2022","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Wu et al., 2022] Jiaxi Wu, Jiaxin Chen, et al. Target- relevant knowledge preservation for multi-source domain adaptive object detection. In CVPR, 2022.","title":"Targetrelevant knowledge preservation for multi-source domain adaptive object detection","authors":"Wu","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Xu et al., 2018] Ruijia Xu, Ziliang Chen, et al. Deep cock- tail network: Multi-source unsupervised domain adapta- tion with category shift. In CVPR, 2018.","title":"Deep cocktail network: Multi-source unsupervised domain adaptation with category shift","authors":"Xu","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Yang et al., 2020] Luyu Yang, Yogesh Balaji, et al. Curricu- lum manager for source selection in multi-source domain adaptation. In ECCV, 2020.","title":"Curriculum manager for source selection in multi-source domain adaptation","authors":"Yang","year":"2020","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Yao et al., 2021a] Xingxu Yao, Sicheng Zhao, Pengfei Xu, and Jufeng Yang. Multi-source domain adaptation for ob- ject detection. In ICCV, 2021.","title":"Multi-source domain adaptation for object detection","authors":"Yao","year":"2021","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Yao et al., 2021b] Yuan Yao, Xutao Li, et al. Multisource heterogeneous domain adaptation with conditional weight- ing adversarial network. IEEE TNNLS, 2021.","title":"Multisource heterogeneous domain adaptation with conditional weighting adversarial network","authors":"Yao","year":"2021","venue":"IEEE TNNLS","doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Yin et al., 2022] Yueming Yin, Zhen Yang, Haifeng Hu, and Xiaofu Wu. Universal multi-source domain adaptation for image classification. PR, 2022.","title":"Universal multi-source domain adaptation for image classification","authors":"Yueming Yin, Zhen Yang, Haifeng Hu, Xiaofu Wu","year":"2022","venue":"PR","doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[Yuan et al., 2022] Jin Yuan, Feng Hou, et al. Self- supervised graph neural network for multi-source domain adaptation. In ACM MM, 2022.","title":"Selfsupervised graph neural network for multi-source domain adaptation","authors":"Jin Yuan, Feng Hou","year":"2022","venue":"ACM MM","doi":null,"url":null,"internal_url":null},{"position":57,"raw":"[Zhang et al., 2022] Dan Zhang, Mao Ye, Yiguang Liu, Lin Xiong, and Lihua Zhou. Multi-source unsupervised do- main adaptation for object detection. INFFUS, 2022.","title":"Multi-source unsupervised domain adaptation for object detection","authors":"Dan Zhang, Mao Ye, Yiguang Liu, Lin Xiong, Lihua Zhou","year":"2022","venue":"INFFUS","doi":null,"url":null,"internal_url":null},{"position":58,"raw":"[Zhang et al., 2023] Wen Zhang, Lingfei Deng, Lei Zhang, and Dongrui Wu. A survey on negative transfer. IEEE/CAA JAS, 2023.","title":"A survey on negative transfer","authors":"Wen Zhang, Lingfei Deng, Lei Zhang, Dongrui Wu","year":"2023","venue":"IEEE/CAA JAS","doi":null,"url":null,"internal_url":null},{"position":59,"raw":"[Zhao et al., 2018] Han Zhao, Shanghang Zhang, et al. Ad- versarial multiple source domain adaptation. In NIPS, 2018.","title":"Adversarial multiple source domain adaptation","authors":"Han Zhao, Shanghang Zhang","year":"2018","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":60,"raw":"[Zhao et al., 2019] Sicheng Zhao, Bo Li, et al. Multi-source domain adaptation for semantic segmentation. In NeurIPS, 2019.","title":"Multi-source domain adaptation for semantic segmentation","authors":"Sicheng Zhao, Bo Li","year":"2019","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":61,"raw":"[Zhao et al., 2020] Sicheng Zhao, Guangzhi Wang, et al. Multi-source distilling domain adaptation. In AAAI, 2020.","title":"Multi-source distilling domain adaptation","authors":"Sicheng Zhao, Guangzhi Wang","year":"2020","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":62,"raw":"[Zhao et al., 2021a] Sicheng Zhao, Bo Li, et al. Madan: multi-source adversarial domain aggregation network for domain adaptation. IJCV, 2021.","title":"Madan: multi-source adversarial domain aggregation network for domain adaptation","authors":"Zhao","year":"2021","venue":"IJCV","doi":null,"url":null,"internal_url":null},{"position":63,"raw":"[Zhao et al., 2021b] Sicheng Zhao, Yang Xiao, et al. Cur- riculum cyclegan for textual sentiment domain adaptation with multiple sources. In WWW, 2021.","title":"Curriculum cyclegan for textual sentiment domain adaptation with multiple sources","authors":"Zhao","year":"2021","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":64,"raw":"[Zhao et al., 2022] Sicheng Zhao, Xiangyu Yue, et al. A re- view of single-source deep unsupervised visual domain adaptation. IEEE TNNLS, 2022.","title":"A review of single-source deep unsupervised visual domain adaptation","authors":"Sicheng Zhao, Xiangyu Yue","year":"2022","venue":"IEEE TNNLS","doi":null,"url":null,"internal_url":null},{"position":65,"raw":"[Zhao et al., 2023] Sicheng Zhao, Xiaopeng Hong, et al. To- ward label-efficient emotion and sentiment analysis. Proc. IEEE, 2023.","title":"Toward label-efficient emotion and sentiment analysis","authors":"Sicheng Zhao, Xiaopeng Hong","year":"2023","venue":"Proc. IEEE","doi":null,"url":null,"internal_url":null},{"position":66,"raw":"[Zhao et al., 2024] Sicheng Zhao, Huizai Yao, et al. Multi- source-free domain adaptive object detection. IJCV, 2024.","title":"Multisource-free domain adaptive object detection","authors":"Sicheng Zhao, Huizai Yao","year":"2024","venue":"IJCV","doi":null,"url":null,"internal_url":null},{"position":67,"raw":"[Zhou et al., 2022] Lihua Zhou, Mao Ye, et al. Prototype- based multisource domain adaptation. IEEE TNNLS, 2022.","title":"Prototypebased multisource domain adaptation","authors":"Lihua Zhou, Mao Ye","year":"2022","venue":"IEEE TNNLS","doi":null,"url":null,"internal_url":null},{"position":68,"raw":"[Zhu et al., 2019] Yongchun Zhu, Fuzhen Zhuang, and De- qing Wang. Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources. In AAAI, 2019.","title":"Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources","authors":"Yongchun Zhu, Fuzhen Zhuang, Deqing Wang","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":69,"raw":"[Zuo et al., 2021] Yukun Zuo, Hantao Yao, and Changsheng Xu. Attention-based multi-source domain adaptation. IEEE TIP, 2021.","title":"Attention-based multi-source domain adaptation","authors":"Yukun Zuo, Hantao Yao, Changsheng Xu","year":"2021","venue":"IEEE TIP","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}