Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification

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@Article{Ying_Bi:ieeeTEC,

• author = "Ying Bi and Bing Xue and Mengjie Zhang",
• title = "Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification",
• journal = "IEEE Transactions on Evolutionary Computation",
• note = "Accepted for future publication",
• keywords = "genetic algorithms, genetic programming, ANN, Evolutionary Deep Learning, Image Classification, Small Data, Evolutionary Computation, Deep Learning",
• ISSN = "1089-778X",
• URL = "https://ieeexplore.ieee.org/abstract/document/9919314/",
• DOI = "doi:10.1109/TEVC.2022.3214503",
• size = "15 pages",
• 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