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Combat data shift in few-shot learning with knowledge graph

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Abstract

Many few-shot learning approaches have been designed under the meta-learning framework, which learns from a variety of learning tasks and generalizes to new tasks. These meta-learning approaches achieve the expected performance in the scenario where all samples are drawn from the same distributions (i.i.d. observations). However, in real-world applications, few-shot learning paradigm often suffers from data shift, i.e., samples in different tasks, even in the same task, could be drawn from various data distributions. Most existing few-shot learning approaches are not designed with the consideration of data shift, and thus show downgraded performance when data distribution shifts. However, it is nontrivial to address the data shift problem in few-shot learning, due to the limited number of labeled samples in each task. Targeting at addressing this problem, we propose a novel metric-based meta-learning framework to extract task-specific representations and task-shared representations with the help of knowledge graph. The data shift within/between tasks can thus be combated by the combination of task-shared and task-specific representations. The proposed model is evaluated on popular benchmarks and two constructed new challenging datasets. The evaluation results demonstrate its remarkable performance.

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Acknowledgements

The research work was supported by the National Natural Science Foundation of China (Grant Nos. 62176014, U1836206, 61773361, U1811461).

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Authors and Affiliations

  1. Institute of Computing Technology, CAS, Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Beijing, 100190, China

    Yongchun Zhu, Zhiyuan Qi, Juan Cao & Qing He

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yongchun Zhu, Juan Cao & Qing He

  3. Institute of Artificial Intelligence, Beihang University, Beijing, 100191, China

    Fuzhen Zhuang

  4. Xiamen Institute of Data Intelligence, Xiamen, 361021, China

    Fuzhen Zhuang

  5. Computer Science and Engineering, University of Notre Dame, South Bend, IN, 46556, USA

    Xiangliang Zhang

  6. University of California San Diego, La Jolla, CA, 92093, USA

    Zhiyuan Qi

  7. College of Information Engineering & Academy for Multidisciplinary Studies, Capital Normal University, Beijing, 100089, China

    Zhiping Shi

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  1. Yongchun Zhu
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  2. Fuzhen Zhuang
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  5. Zhiping Shi
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  6. Juan Cao
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  7. Qing He
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Corresponding author

Correspondence to Fuzhen Zhuang.

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Yongchun Zhu is currently pursuing his MS degree in the Institute of Computing Technology, Chinese Academy of Sciences, China. He has published more than 10 papers in journals and conference proceedings including KDD, AAAI, WWW, SIGIR and so on. He received his BS degree from Beijing Normal University, China in 2018. His main research interests include transfer learning, meta learning and recommendation system.

Fuzhen Zhuang is a professor in Institute of Artificial Intelligence, Beihang University, China. His research interests include transfer learning, machine learning, data mining, multi-task learning and recommendation systems. He has published more than 100 papers in the prestigious refereed journals and conference proceedings, such as IEEE TKDE, IEEE Transactions on Cybernetics, IEEE TNNLS, ACM TIST, SIGKDD, IJCAI, AAAI, WWW, and ICDE.

Xiangliang Zhang is currently an Associate Professor and directs the Machine Intelligence and Knowledge Engineering (MINE) Laboratory at the Department of Computer Science and Engineering in University of Notre Dame, USA. She received the PhD degree in computer science from INRIA-University Paris-Sud, France in July 2010. She has authored or co-authored over 170 refereed papers in various journals and conferences. Her current research interests lie in designing machine learning algorithms for learning from complex and large-scale streaming data and graph data.

Zhiyuan Qi is currently pursuing his MS degree in the University of California, USA. He received the BE degree in software engineering from Sun Yatsen University, China in 2019. He has published several papers in journals and conference proceedings including Proceedings of the IEEE, IEEE Computational Intelligence Magazine, Neu-Zhiping Shi is currently a professor in the College of Information Engineering at the Capital Normal University, China. From 2005 to 2010, he was on the faculty at the Institute of Computing Technology, Chinese Academy of Sciences where he received his PhD degree in computer software and theory in 2005. His research interests include formal verification and visual information analysis. He is the (co-) author of more than 100 research papers. He is a Member of the IEEE and the ACM.

Juan Cao received the PhD degree from the Institute of Computing Technology, Chinese Academy of Sciences, China in 2008. She is currently working as an Professor with the Institute of Computing Technology, Chinese Academy of Sciences, China. Her research interests include multimedia content analysis, fake news detection, and forgery detection.

Qing He is a Professor in the Institute of Computing Technology, Chinese Academy of Science (CAS), and a Professor at the Graduate University of Chinese (GUCAS), China. He received the BS degree from Hebei Normal University, China in 1985, and the MS degree from Zhengzhou University, China in 1987, both in mathematics. He received the PhD degree in 2000 from Beijing Normal University, China in fuzzy mathematics and artificial intelligence, China. From 1987 to 1997, he had been with Hebei University of Science and Technology, China. He is currently a doctoral tutor at the Institute of Computing and Technology, CAS, China. His interests include data mining, machine learning, classification, and fuzzy clustering.

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Zhu, Y., Zhuang, F., Zhang, X. et al. Combat data shift in few-shot learning with knowledge graph. Front. Comput. Sci. 17, 171305 (2023). https://doi.org/10.1007/s11704-022-1339-7

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