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Appl."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>\n            Few-shot learning (FSL) aims to classify a novel object into a specific category under limited training samples. This is a challenging task since (1) the features expressed by pre-trained knowledge introduce perceived bias and then constrain the classification space, and (2) the use of general hallucination techniques based on global features fails to escape the limited classification space, resulting in sub-optimal improvements. To solve these issues, this article proposes an interventional feature generation (IFG) method. Specifically, we first use the relations of the categories or instances as interventional operations to implicitly constrain the feature representations (pre-trained knowledge) into different classification subsets. Then, we employ a parameter-free feature generation strategy to enrich each subset\u2019s training samples of the support category. In other words, IFG provides a multi-subsets learning strategy to reduce the influence of perceived bias, enrich the diversity of generated features, and improve the robustness of the few-shot classifier. We apply our method to four benchmark datasets and observe state-of-the-art performance across all experiments. Specifically, compared to the baseline on the Mini-ImageNet dataset, our approach yields accuracy improvements of 6.03% and 3.46% for 1 and 5 support training samples, respectively. Furthermore, the proposed interventional feature generation technique can improve classifier performance in other FSL methods, demonstrating its versatility and potential for broader applications. The code is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/ShuoWangCS\/IFG-FSL\/\">https:\/\/github.com\/ShuoWangCS\/IFG-FSL\/<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3729171","type":"journal-article","created":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T16:00:21Z","timestamp":1744300821000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Interventional Feature Generation for Few-shot Learning"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4881-9344","authenticated-orcid":false,"given":"Shuo","family":"Wang","sequence":"first","affiliation":[{"name":"School of Data Science, School of Information Science and Technology, University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7322-9942","authenticated-orcid":false,"given":"Jinda","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Technology, University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7946-8199","authenticated-orcid":false,"given":"Huixia","family":"Ben","sequence":"additional","affiliation":[{"name":"School of Public Security and Emergency Management, Anhui University of Science and Technology, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0695-1566","authenticated-orcid":false,"given":"Yanbin","family":"Hao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, School of Artificial Intelligence, Hefei University of Technology, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4660-8092","authenticated-orcid":false,"given":"Xingyu","family":"Gao","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3094-7735","authenticated-orcid":false,"given":"Meng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, School of Artificial Intelligence, Hefei University of Technology, Hefei, China"}]}],"member":"320","published-online":{"date-parts":[[2025,7]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00891"},{"key":"e_1_3_1_3_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Bertinetto Luca","year":"2019","unstructured":"Luca Bertinetto, Joao F. 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