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Multi-level adaptive feature representation based on task augmentation for Cross-Domain Few-Shot learning

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Abstract

Cross-Domain Few-Shot Learning (CDFSL) is one of the most cutting-edge fields in machine learning. It not only addresses the traditional few-shot problem but also allows for different distributions between base classes and novel classes. However, most current CDFSL models only focus on the generalization performance of high-level features during training and testing, which hinders their ability to generalize well to domains with significant gaps. To overcome this problem, we propose a CDFSL method based on Task Augmentation and Multi-Level Adaptive features representation(TA-MLA). At the feature representation level, we introduce a meta-learning strategy for multi-level features and adaptive features. The former come from different layers of network. They jointly participate in image prediction to fully explore transferable features suitable for cross-domain scenarios. The latter is based on a feature adaptation module of feed-forward attention, aiming to learn domain-adaptive features to improve the generalization of the model. At the training task level, we employ a plug-and-play Task Augmentation(TA) module to generate challenging tasks with adaptive inductive biases, thereby expanding the distribution of the source domain and further bridging domain gaps. Extensive experiments conducted on multiple datasets. The results demonstrate that our method based on meta-learning can effectively improves few-shot classification performance, especially in cases with significant domain shift.

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Data Availability

The five datasets used and analysed during the current study are available at https://image-net.org/, https://www.kaggle.com/saroz014/plant-disease/, http://madm.dfki.de/downloads, http://challenge2018.isic-archive.com, https://www.kaggle.com/nih-chest-xrays/data, http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz and http://imagenet.stanford.edu/internal/car196/cars_train.tgz

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Acknowledgements

The authors thank the anonymous referees for their valuable comments. This paper is in part supported by the National Natural Science Foundation of China under Grants 62376231, the Sichuan Science and Technology Program 2023YFS0202 and 2023YFG0267, respectively.

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Ling Yue and Lin Feng drafted the manuscript. Ling Yue completed the code. Qiuping Shuai, Zihao Li and Lingxiao Xu designed experiments. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Lin Feng.

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Yue, L., Feng, L., Shuai, Q. et al. Multi-level adaptive feature representation based on task augmentation for Cross-Domain Few-Shot learning. Appl Intell 55, 291 (2025). https://doi.org/10.1007/s10489-024-06110-9

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