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Found 3 papers, 2 papers with code
Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification
While existing approaches have achieved competitive performance in image classification, they are not well suited to problems where the computational budget is limited for two reasons: (1) the obtained architectures are either solely optimized for classification performance, or only for one deployment scenario; (2) the search process requires vast computational resources in most approaches.
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NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm
This paper introduces NSGA-Net -- an evolutionary approach for neural architecture search (NAS).
NSGA-Net: A Multi-Objective Genetic Algorithm for Neural Architecture Search
This paper introduces NSGA-Net, an evolutionary approach for neural architecture search (NAS).
