Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification
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- @Article{Ying_Bi:ieeeTEC,
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author = "Ying Bi and Bing Xue and Mengjie Zhang",
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title = "Genetic Programming-Based Evolutionary Deep Learning
for Data-Efficient Image Classification",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2024",
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volume = "28",
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number = "2",
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pages = "307--322",
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month = apr,
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keywords = "genetic algorithms, genetic programming, ANN,
Evolutionary Deep Learning, Image Classification, Small
Data, Evolutionary Computation, Deep Learning",
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ISSN = "1089-778X",
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URL = "https://ieeexplore.ieee.org/abstract/document/9919314/",
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DOI = "doi:10.1109/TEVC.2022.3214503",
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size = "15 pages",
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abstract = "Data-efficient image classification is a challenging
task that aims to solve image classification using
small training data. Neural network-based deep learning
methods are effective for image classification, but
they typically require large-scale training data and
have major limitations such as requiring expertise to
design network architectures and having poor
interpretability. Evolutionary deep learning is a
recent hot topic that combines evolutionary computation
with deep learning. However, most evolutionary deep
learning methods focus on evolving architectures of
neural networks, which still suffers from limitations
such as poor interpretability. We propose a new genetic
programming-based evolutionary deep learning approach
to data-efficient image classification. The new
approach can automatically evolve variable-length
models using many important operators from both image
and classification domains. It can learn different
types of image features from colo",
-
notes = "Also known as \cite{9919314}",
- }
Genetic Programming entries for
Ying Bi
Bing Xue
Mengjie Zhang
Citations