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Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3263721
Abstract: Few shot models have started to gain a lot of popularity in the past few years. This is mostly because these models grant the ability to structure the representation space (classes) using a very less…
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Keywords:
enhanced prototypical;
handwritten urdu;
prototypical network;
shot ... See more keywords
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Published in 2022 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2022.3152686
Abstract: Few-shot image classification (FSIC) is the task of generalizing a model to unknown categories by learning from a small number of labeled samples of some given categories. Recently, metric-based approaches have received lots of attention…
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Keywords:
shot image;
bidirectional matching;
model;
image classification ... See more keywords
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Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3232394
Abstract: In this article, we study the challenging few-shot fault diagnosis (FSFD) problem where limited faulty samples are available. Metric-based meta-learning methods have been a prevalent approach toward FSFD; however, most of them rely on learning…
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Keywords:
fault diagnosis;
shot fault;
regularized prototypical;
reweighted regularized ... See more keywords
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Published in 2023 at "Symmetry"
DOI: 10.3390/sym15040903
Abstract: Methods for fault diagnosis based on metric learning, in which a query sample is classified by picking the closest prototype from the support set based on their feature similarities, have been the subject of many…
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Keywords:
circulating pumps;
fault diagnosis;
uncertainty;
prototypical network ... See more keywords