EENA: Efficient Evolution of Neural Architecture
This addresses the high computational expense of architecture search for machine learning practitioners, though it is incremental as it builds on existing evolutionary approaches.
The paper tackles the problem of inefficient neural architecture search by proposing EENA, an evolutionary method that uses guided mutation and crossover to reduce computational cost, achieving 2.56% test error on CIFAR-10 with only 0.65 GPU-days.
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.