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EENA: Efficient Evolution of Neural Architecture
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Latest algorithms for automatic neural architecture search perform remarkable but are basically directionless in search space and computational expensive in training of every intermediate architecture. In this paper, we propose a method for efficient architecture search called EENA (Efficient Evolution of Neural Architecture). Due to the elaborately designed mutation and crossover operations, the evolution process can be guided by the information have already been learned. Therefore, less computational effort will be required while the searching and training time can be reduced significantly. On CIFAR-10 classification, EENA using minimal computational resources (0.65 GPU-days) can design highly effective neural architecture which achieves 2.56% test error with 8.47M parameters. Furthermore, the best architecture discovered is also transferable for CIFAR-100.
Forward citations
Cited by 2 Pith papers
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Rethinking the Number of Channels for the Convolutional Neural Network
A width-search method built on function-preserving widening finds CIFAR channel patterns that reduce parameters and slightly lower test error relative to standard network schedules.
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AutoML: A Survey of the State-of-the-Art
A survey that organizes AutoML into a four-stage pipeline and reviews neural architecture search methods, their performance, and open problems.
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