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Deductively coding psychosocial autopsy interview data using a few-shot learning large language model. [PDF]
Front Public HealthBalt E +8 more
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Enhancing Melanoma Diagnosis: Integration of Zero-Shot and Few-Shot Learning With Large Language Models. [PDF]
Skin Res TechnolNagaoka T.
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A Novel Few-Shot Learning Framework Based on Diffusion Models for High-Accuracy Sunflower Disease Detection and Classification. [PDF]
Plants (Basel)Zhou H +8 more
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An Efficient Model for Leafy Vegetable Disease Detection and Segmentation Based on Few-Shot Learning Framework and Prototype Attention Mechanism. [PDF]
Plants (Basel)Hai T +10 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
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2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019
We propose a variational Bayesian framework for enhancing few-shot learning performance. This idea is motivated by the fact that single point based metric learning approaches are inherently noise-vulnerable and easy-to-be-biased. In a nutshell, stochastic variational inference is invoked to approximate bias-eliminated class specific sample ...
Jian Zhang +4 more
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We propose a variational Bayesian framework for enhancing few-shot learning performance. This idea is motivated by the fact that single point based metric learning approaches are inherently noise-vulnerable and easy-to-be-biased. In a nutshell, stochastic variational inference is invoked to approximate bias-eliminated class specific sample ...
Jian Zhang +4 more
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IEEE Transactions on Neural Networks and Learning Systems
Forming deep feature embeddings is an effective method for few-shot learning (FSL). However, in the case of insufficient samples, overcoming the task complexity while improving the accuracy is still a major challenge. To address this problem, this article considers the consistency between similar data from the fractal perspective, introduces a priori ...
Fobao Zhou, Wenkai Huang
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Forming deep feature embeddings is an effective method for few-shot learning (FSL). However, in the case of insufficient samples, overcoming the task complexity while improving the accuracy is still a major challenge. To address this problem, this article considers the consistency between similar data from the fractal perspective, introduces a priori ...
Fobao Zhou, Wenkai Huang
openaire +2 more sources