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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 0079 +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 0079 +4 more
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Unsupervised meta-learning for few-shot learning
Pattern Recognition, 2021Abstract Meta-learning is an effective tool to address the few-shot learning problem, which requires new data to be classified considering only a few training examples. However, when used for classification, it requires large labeled datasets, which are not always available in practice.
Hui Xu +4 more
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Meta-Transfer Learning for Few-Shot Learning
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 CVPR ...
Tat-Seng Chua, Yaoyao Liu, Bernt Schiele
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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 0001
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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 0001
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Learning to Compare: Relation Network for Few-Shot Learning [PDF]
, 2018 We present a conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each.
Tao Xiang +2 more
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Exploring Quantization in Few-Shot Learning
2020 18th IEEE International New Circuits and Systems Conference (NEWCAS), 2020Training the neural networks on chip, which enables the local privacy data to be stored and processed at edge platforms, is earning vital importance with the explosive growth of Internet of Things (IoT). Although the on-chip training has been widely investigated in previous arts, there are few works related to the on-chip learning of Few-Shot Learning (
Meiqi Wang +3 more
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A few shots at few shot learning
Automatic Target Recognition XXXIII, 2023Donald Waagen, Don Hulsey, David Gray
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Co-Learning for Few-Shot Learning
Neural Processing Letters, 2022Rui Xu 0012 +5 more
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Few-Shot Learning with Novelty Detection
Machine learning has achieved considerable success in data-intensive applications, yet encounters challenges when confronted with small datasets. Recently, few-shot learning (FSL) has emerged as a promising solution to address this limitation. By leveraging prior knowledge, FSL exhibits the ability to swiftly generalize to new tasks, even when ...Kim Bjerge +2 more
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Machine Learning in Epigenetic Diseases
Medical Epigenetics, 2021Robert E Akins, Karyn Robinson
exaly