Hierarchical Representations for Efficient Architecture Search
This work addresses the problem of automating architecture design for neural networks, offering a competitive and faster alternative to existing methods, though it is incremental in improving efficiency.
The paper tackles efficient neural architecture search by proposing a hierarchical evolutionary algorithm that discovers architectures outperforming many manually designed models, achieving top-1 errors of 3.6% on CIFAR-10 and 20.3% on ImageNet, with random search reducing search time from 36 hours to 1 hour at a slight accuracy cost.
We explore efficient neural architecture search methods and show that a simple yet powerful evolutionary algorithm can discover new architectures with excellent performance. Our approach combines a novel hierarchical genetic representation scheme that imitates the modularized design pattern commonly adopted by human experts, and an expressive search space that supports complex topologies. Our algorithm efficiently discovers architectures that outperform a large number of manually designed models for image classification, obtaining top-1 error of 3.6% on CIFAR-10 and 20.3% when transferred to ImageNet, which is competitive with the best existing neural architecture search approaches. We also present results using random search, achieving 0.3% less top-1 accuracy on CIFAR-10 and 0.1% less on ImageNet whilst reducing the search time from 36 hours down to 1 hour.