{"type":"ScholarlyArticle","title":"Learning to Learn from Corrupted Data for Few-Shot Learning","authors":[{"name":"Yuexuan An","slug":"yuexuan-an","given":"Yuexuan","family":"An","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing , 210096 , China; MOE Key Laboratory of Computer Network and Information Integration ( Southeast University) , China"},{"name":"Xingyu Zhao","slug":"xingyu-zhao","given":"Xingyu","family":"Zhao","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing , 210096 , China; MOE Key Laboratory of Computer Network and Information Integration ( Southeast University) , China"},{"name":"Hui Xue","slug":"hui-xue","given":"Hui","family":"Xue","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing , 210096 , China; MOE Key Laboratory of Computer Network and Information Integration ( Southeast University) , China"}],"abstract":"Few-shot learning which aims to generalize knowledge learned from annotated base training data to recognize unseen novel classes has attracted considerable attention. Existing few-shot methods rely on completely clean training data. However, in the real world, the training data are always corrupted and accompanied by noise due to the disturbance in data transmission and low-quality annotation, which severely degrades the performance and generalization capability of few-shot models. To address the problem, we propose a unified peer-collaboration learning (PCL) framework to extract valid knowledge from corrupted data for few-shot learning. PCL leverages two modules to mimic the peer collaboration process which cooperatively evaluates the importance of each sample. Specifically, each module first estimates the importance weights of different samples by encoding the information provided by the other module from both global and local perspectives. Then, both modules leverage the obtained importance weights to guide the reevaluation of the loss value of each sample. In this way, the peers can mutually absorb knowledge to improve the robustness of few-shot models. Experiments verify that our framework combined with different few-shot methods can significantly improve the performance and robustness of original models.","doi":"10.24963/ijcai.2023/381","url":"https://ijcai.org/proceedings/2023/381","pdf":"https://ijcai.org/proceedings/2023/0381.pdf","markdown":"https://ijcai.org/proceedings/2023/381.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/381","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":381,"pages":{"first":3423,"last":3431},"keywords":["Machine Learning: ML: Few-shot learning"],"videos":[],"references":[{"position":1,"raw":"[An et al., 2021] Yuexuan An, Hui Xue, Xingyu Zhao, and Lu Zhang. 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