EENA: Efficient Evolution of Neural Architecture
By Hui Zhu, Zhulin An, Chuanguang Yang, Kaiqiang Xu, Yongjun Xu. 2019
47 citations recorded by the source corpus.
Latest algorithms for automatic neural architecture search perform remarkable but are basically directionless in search space and computational expensive in the 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.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neural Architecture Search & AutoML
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Citations in the corpus
- A Survey on Evolutionary Neural Architecture Search (2020, background): “objective EA GAs [9], [19]–[23], [33]–[35], [47], [49], [52], [56], [59]–[61], [63], [65]–[67], [69]–[71], [74], [78], [81], [82], [85], [86], [88], [90], [92], [93], [95], [97]– [99], [103], [107], [110], [113], [117]– [119], [121], [122], [125]–[127], [131], [138], [143], [144] [39], [145] [44], [”