个人信息

李爽博士，永利皇宫463cc长聘副教授、特别研究员、博士生导师。分别于2012年、2018年自东北大学、清华大学自动化系获得工学学士、博士学位，于2015年-2016年在美国康奈尔大学永利皇宫463cc进行学术访问，于2016年-2017年在微软亚洲研究院实习。主要研究方向为机器学习、深度学习与迁移学习等。其研究成果发表在国际顶级期刊IEEE TPAMI, TIP, TKDE,TCYB TNNLS等和顶级会议NeurIPS, ICLR, CVPR, AAAI, ACM MM等近50篇，其中CCF-A类论文31篇（一作/通讯28篇），得到国内外学术界和工业界的广泛关注。在科研领域，李爽博士与美国康奈尔大学、UC Berkeley、英国爱丁堡大学、新加坡国立、新加坡南洋理工大学、清华大学等国内外顶尖学校；微软亚洲研究院（MSRA）、阿里达摩院等知名研究机构有长期的交流和合作，保证科研的前沿性与实用性。

每年招收1名博士生、2~3名硕士研究生和多名致力于学术研究的本科生，一起研究和发表高水平论文。其中，具有ACM程序设计竞赛、编程竞赛、数学建模竞赛或者其它科研背景的学生将优先考虑。实验室师生关系融洽，共同成长，欢迎大家联系和交流讨论。

更多详细信息请前往个人主页：shuangli.xyz

科研方向

1、 迁移学习 (Transfer Learning / Domain Adaptation) 算法与应用

2、 机器学习、深度学习、强化学习算法与应用

3、 无人驾驶、智慧医疗、工业质检中的计算机视觉应用

代表性学术成果

[1] Shuang Li, Chi Harold Liu, Qiuxia Lin, Qi Wen, Limin Su, Gao Huang and Zhengming Ding. “Deep Residual Correction Network for Partial Domain Adaptation”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021, [IF: 24.314], SCI 1区, CCF-A.

[2] Shuang Li, Binhui Xie, Qiuxia Lin, Chi Harold Liu, Gao Huang and Guoren Wang. “Generalized Domain Conditioned Adaptation Network”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022, [IF: 24.314], SCI 1区, CCF-A.

[3] Binhui Xie, Shuang Li*, Mingjia Li, Chi Harold Liu, Gao Huang and Guoren Wang. “SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023, [IF: 24.314], SCI 1区, CCF-A.

[4] Shuang Li, Wenxuan Ma, Jinming Zhang, Chi Harold Liu, Jian Liang, Guoren Wang. “Meta-reweighted Regularization for Unsupervised Domain Adaptation”, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021, [IF: 9.235], SCI 1区, CCF-A.

[5] Shuang Li, Shugang Li, Mixue Xie, Kaixiong Gong, Jianxin Zhao, Chi Harold Liu, Guoren Wang. “End-to-End Transferable Anomaly Detection via Multi-spectral Cross-domain Representation Alignment”, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021, [IF: 9.235], SCI 1区, CCF-A.

[6] Binhui Xie, Shuang Li*, Fangrui Lv, Chi Harold Liu, Guoren Wang, Dapeng Wu. “A Collaborative Alignment Framework of Transferable Knowledge Extraction for Unsupervised Domain Adaptation”, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022, [IF: 9.235], SCI 1区, CCF-A.

[7] Shuang Li, Shiji Song, Gao Huang, Zhengming Ding, Cheng Wu, “Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation”, IEEE Transactions on Image Processing (TIP) 27(9): 4260-4273 (2018). [IF: 9.34], SCI 1区, CCF-A.

[8] Yiming Chen , Shiji Song , Shuang Li*, Cheng Wu. “A Graph Embedding Framework for Maximum Mean Discrepancy Based Domain Adaptation Algorithms”. IEEE Transactions on Image Processing (TIP) 29:199-213 (2020), [IF: 11.041], SCI 1区, CCF-A.

[9] Shuang Li, Kaixiong Gong, Binhui Xie, Chi Harold Liu, Weipeng Cao, Song Tian. “Critical Classes and Samples Discovering for Partial Domain Adaptation”. IEEE Transactions on Cybernetics (TCYB), 2022. [IF: 19.118], SCI 1区.

[10] Shuang Li, Chi Harold Liu, Limin Su, Binhui Xie, Zhengming Ding, C. L. Philip Chen, Dapeng Wu. “Discriminative Transfer Feature and Label Consistency for Cross-Domain Image Classification”, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 31(11): 4842-4856 (2020), SCI 1区.

[11] Ying Zhao, Shuang Li*, Rui Zhang, Chi Harold Liu, Weipeng Cao, Xizhao Wang, Song Tian. “Semantic Correlation Transfer for Heterogeneous Domain Adaptation”. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022, [IF: 14.255], SCI 1区.

[12] Zhenjie Yu, Shuang Li*, Yirui Shen, Chi Harold Liu, Shuigen Wang. "On the Difficulty of Unpaired Infrared-to-Visible Video Translation: Fine-Grained Content-Rich Patches Transfer", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023, CCF-A.

[13] Mixue Xie, Shuang Li*, Rui Zhang, Chi Harold Liu. "Dirichlet-based Uncertainty Calibration for Active Domain Adaptation", International Conference on Learning Representations (ICLR), 2023, (Spotlight).

[14] Mingjia Li, Binhui Xie, Shuang Li*, Chi Harold Liu, Xinjing Cheng. "VBLC: Visibility Boosting and Logit-Constraint Learning for Domain Adaptive Semantic Segmentation under Adverse Conditions", AAAI Conference on Artificial Intelligence (AAAI), 2023, (Oral Presentation), CCF-A.

[15] Kaixiong Gong, Shuang Li*, Shugang Li, Rui Zhang, Chi Harold Liu, Qiang Chen. "Improving Transferability for Domain Adaptive Detection Transformers", ACM Multimedia (ACM MM), 2022, CCF-A.

[16] Zhenjie Yu, Kai Chen, Shuang Li*, Bingfeng Han, Chi Harold Liu, Shuigen Wang. "ROMA: Cross-Domain Region Similarity Matching for Unpaired Nighttime Infrared to Daytime Visible Video Translation", ACM Multimedia (ACM MM), 2022, CCF-A.

[17] Wenxuan Ma, Jinming Zhang, Shuang Li*, Chi Harold Liu, Yulin Wang, Wei Li. "Making The Best of Both Worlds: A Domain-Oriented Transformer for Unsupervised Domain Adaptation", ACM Multimedia (ACM MM), 2022, CCF-A.

[18] Fangrui Lv, Jian Liang, Shuang Li*, Bin Zang, Chi Harold Liu, Ziteng Wang, Di Liu. "Causality Inspired Representation Learning for Domain Generalization", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022, (Oral Presentation), CCF-A.

[19] Binhui Xie, Longhui Yuan, Shuang Li*, Chi Harold Liu, Xinjing Cheng. "Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022, (Oral Presentation), CCF-A.

[20] Binhui Xie, Longhui Yuan, Shuang Li*, Chi Harold Liu, Xinjing Cheng, Guoren Wang. “Active Learning for Domain Adaptation: An Energy-based Approach”, AAAI Conference on Artificial Intelligence (AAAI), 2022, CCF-A.

[21] Fangrui Lv, Jian Liang, Kaixiong Gong, Shuang Li*, Chi Harold Liu, Han Li, Di Liu, Guoren Wang. "Pareto Domain Adaptation", Neural Information Processing Systems (NeurIPS), 2021, CCF-A.

[22] Shuang Li, Mixue Xie, Fangrui Lv, Chi Harold Liu, Jian Liang, Chen Qin, Wei Li. "Semantic Concentration for Domain Adaptation", International Conference on Computer Vision (ICCV), 2021, CCF-A.

[23] Shuang Li, Bingfeng Han, Zhenjie Yu, Chi Harold Liu, Kai Chen, Shuigen Wang. "I2V-GAN: Unpaired Infrared-to-Visible Video Translation", ACM Multimedia (ACM MM), 2021, CCF-A.

[24] Qi Wen, Shuang Li, Bingfeng Han, Yi Yuan. "ZiGAN: Fine-grained Chinese Calligraphy Font Generation via a Few-shot Style Transfer Approach", ACM Multimedia (ACM MM), 2021, CCF-A.

[25] Shuang Li, Mixue Xie, Kaixiong Gong, Chi Harold Liu, Yulin Wang, Wei Li. "Transferable Semantic Augmentation for Domain Adaptation", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021, (Oral Presentation), CCF-A.

[26] Yan Xia, Yusheng Xu, Shuang Li, Rui Wang, Juan Du, Uwe Stilla. "SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021, (Oral Presentation), CCF-A.

[27] Shuang Li, Kaixiong Gong, Chi Harold Liu, Yulin Wang, Feng Qiao, Xinjing Cheng. "MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021, CCF-A.

[28] Shuang Li, Jinming Zhang, Wenxuan Ma, Chi Harold Liu, Wei Li. "Dynamic Domain Adaptation for Efficient Inference", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021, CCF-A.

[29] Shuang Li, Fangrui Lv, Binhui Xie, Chi Harold Liu, Jian Liang, Chen Qin. “Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation”, AAAI Conference on Artificial Intelligence (AAAI), 2021, CCF-A.

[30] Shuang Li, Binhui Xie, Jiashu Wu, Ying Zhao, Chi Harold Liu, Zhengming Ding. "Simultaneous Semantic Alignment Network for Heterogeneous Domain Adaptation", ACM Multimedia (ACM MM) 2020: 3866-3874, CCF-A.

[31] Shuang Li, Chi Harold Liu, Qiuxia Lin, Binhui Xie, Zhengming Ding, Gao Huang, Jian Tang. “Domain Conditioned Adaptation Network”, AAAI Conference on Artificial Intelligence (AAAI), 2020, CCF-A.

[32] Shuang Li, Chi Harold Liu, Binhui Xie, Limin Su, Zhengming Ding, Gao Huang. “Joint Adversarial Domain Adaptation”, ACM Multimedia (ACM MM), 2019: 729-737, CCF-A.

承担科研情况

1. 国家自然科学基金青年项目，“区分度增强的深度领域不变特征选择与修正方法研究”，2020年-2022年，项目负责人

2. 科技部重点研发计划课题，“云边协同工业大数据知识迁移技术”，2021年-2024年，项目负责人

3. 国家自然科学基金联合基金项目，“边缘环境群体智能感知与能力增强关键技术”，2022年-2025年，项目参与人

4. CCF-百度松果基金，“工业视觉质检中的高效迁移学习方法研究”，2022年-2023年，项目负责人

5. 阿里巴巴AIR计划，“基于持续迁移学习的广告业务模型预估方法研究”，2022年-2023年，项目负责人

所获奖励

1. 教育部自然科学一等奖（2023），排名4/8

2. 山东省烟台开发区创新创业领军人才（2021）

3. 北京市优秀毕业生（2018）

4. 清华大学自动化系优秀毕业生（2018）

社会兼职

•社会学术任职：中国计算机学会人工智能与模式识别专委会委员，中国计算机学会多媒体技术专委会委员，中国计算机学会计算机视觉专委会委员，人工智能学会智能控制与智能管理专委会委员

• 期刊审稿人：TPAMI, IJCV, TIP, TKDE, TC, TNNLS, TCYB, TMM, TAI, TSMC

• 会议程序委员会成员：ICLR, CVPR, ICCV, AAAI, ACM Multimedia, IJCAI, ECCV

备注

李爽博士培养的多名在读研究生和本科生近5年已在顶级会议和期刊上发表论文30余篇，欢迎对迁移学习、机器学习和深度学习方向感兴趣的同学联系攻读研究生。实验室师生关系融洽，共同成长，欢迎大家联系和交流讨论。

（更新时间：2023年3月）