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Evolution under Length Constraints for CNN Architecture design

arXiv 2305.03416 ↗First posted 5 May 2023cs.CV cs.NEcited by 0

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Abstract (arXiv)

In recent years, the CNN architectures designed by evolution algorithms have proven to be competitive with handcrafted architectures designed by experts. However, these algorithms need a lot of computational power, which is beyond the capabilities of most researchers and engineers. To overcome this problem, we propose an evolution architecture under length constraints. It consists of two algorithms: a search length strategy to find an optimal space and a search architecture strategy based on genetic algorithm to find the best individual in the optimal space. Our algorithms reduce drastically resource cost and also keep good performance. On the Cifar-10 dataset, our framework presents outstanding performance with an error rate of 5.12% and only 4.6 GPU a day to converge to the optimal individual -22 GPU a day less than the lowest cost automatic evolutionary algorithm in the peer competition.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 52 references that are in the map. First posted on arXiv 5 May 2023.

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28 of 52 references.

References · 28

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16 more
  1. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,097
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