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• W3211144631 abstract "Few-shot learning describes the challenging problem of recognizing samples from unseen classes given very few labeled examples. In many cases, few-shot learning is cast as learning an embedding space that assigns test samples to their corresponding class prototypes. Previous methods assume that data of all few-shot learning tasks comply with a fixed geometrical structure, mostly a Euclidean structure. Questioning this assumption that is clearly difficult to hold in real-world scenarios and incurs distortions to data, we propose to learn a task-aware curved embedding space by making use of the hyperbolic geometry. As a result, task-specific embedding spaces where suitable curvatures are generated to match the characteristics of data are constructed, leading to more generic embedding spaces. We then leverage on intra-class and inter-class context information in the embedding space to generate class prototypes for discriminative classification. We conduct a comprehensive set of experiments on inductive and transductive few-shot learning, demonstrating the benefits of our proposed method over existing embedding methods." @default.
• W3211144631 created "2021-11-08" @default.
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• W3211144631 date "2021-10-01" @default.
• W3211144631 modified "2023-10-18" @default.
• W3211144631 title "Curvature Generation in Curved Spaces for Few-Shot Learning" @default.
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• W3211144631 doi "https://doi.org/10.1109/iccv48922.2021.00857" @default.
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