Dual-Tree Genetic Programming for Few-Shot Image Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{Dual-Tree_Genetic_Programming_for_Few-Shot_Image_Classification,
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author = "Ying Bi and Bing Xue and Mengjie Zhang",
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title = "Dual-Tree Genetic Programming for Few-Shot Image
Classification",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2022",
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volume = "26",
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number = "3",
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pages = "555--569",
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month = jun,
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keywords = "genetic algorithms, genetic programming,
Representation, Fitness Evaluation, Few-Shot Learning,
Image Classification",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2021.3100576",
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size = "15 pages",
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abstract = "Few-shot image classification is an important but
challenging task due to high variations across images
and a small number of training instances. A learning
system often has poor generalisation performance due to
the lack of sufficient training data. Genetic
programming (GP) has been successfully applied to image
classification and achieved promising performance. This
paper proposes a GP-based approach with a dual-tree
representation and a new fitness function to
automatically learn image features for few-shot image
classification. The dual-tree representation allows the
proposed approach to have better search ability and
learn richer features than a single-tree representation
when the number of training instances is very small.
The fitness function based on the classification
accuracy and the distances of the training instances to
the class centroids aims to improve the generalisation
performance. The proposed approach can deal with
different types of few-shot image classification tasks
with various numbers of classes and different",
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notes = "also known as \cite{9499117}
School of Engineering and Computer Science, Victoria
University of Wellington, Wellington 6140, New
Zealand",
- }
Genetic Programming entries for
Ying Bi
Bing Xue
Mengjie Zhang
Citations