Results 231 to 240 of about 514,020 (257)
Unsupervised few shot learning architecture for diagnosis of periodontal disease in dental panoramic radiographs. [PDF]
Sci RepKim MJ, Chae SG, Bae SJ, Hwang KG.
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Ligand-Based Compound Activity Prediction via Few-Shot Learning. [PDF]
J Chem Inf ModelEckmann P, Anderson J, Yu R, Gilson MK.
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Residual channel attention based sample adaptation few-shot learning for hyperspectral image classification. [PDF]
Sci RepZhao Y, Sun J, Hu N, Zai C, Han Y.
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A few-shot learning method for tobacco abnormality identification. [PDF]
Front Plant SciLin H, Qiang Z, Tse R, Tang SK, Pau G.
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One to All: Toward a Unified Model for Counting Cereal Crop Heads Based on Few-Shot Learning. [PDF]
Plant PhenomicsWang Q +6 more
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Expanding continual few-shot learning benchmarks to include recognition of specific instances. [PDF]
PLoS OneKowadlo G +3 more
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Few-Shot Learning with Representative Global Prototype
Neural Networks, 2023Few-shot learning is often challenged by low generalization performance due to the model is mostly learned with the base classes only. To mitigate the above issues, a few-shot learning method with representative global prototype is proposed in this paper. Specifically, to enhance generalization to novel class, we propose a strategy for jointly training
Yukun Liu, Daming Shi, Hexiu Lin
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Splicing learning: A novel few-shot learning approach
Information Sciences, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hu, Lianting, Liang, Huiying, Lu, Long
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2020
Semantic concepts are frequently defined by combinations of underlying attributes. As mappings from attributes to classes are often simple, attribute-based representations facilitate novel concept learning with zero or few examples. A significant limitation of existing attribute-based learning paradigms, such as zero-shot learning, is that the ...
Ren, Mengye +7 more
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Semantic concepts are frequently defined by combinations of underlying attributes. As mappings from attributes to classes are often simple, attribute-based representations facilitate novel concept learning with zero or few examples. A significant limitation of existing attribute-based learning paradigms, such as zero-shot learning, is that the ...
Ren, Mengye +7 more
openaire +1 more source