Efficient Neural Architecture Search via Parameter Sharing
Table 2: Classification errors of ENAS and baselines on CIFAR-10. In this table, the first block presents DenseNet, one of the state-of-the-art architectures designed by human experts. The second block presents approaches that design the entire network. The last block presents techniques that design modular cells which are combined to build the final network.
['[BOLD] Method', '[BOLD] GPUs', '[BOLD] Times', '[BOLD] Params', '[BOLD] Error']
S3SS2SSS0Px4 Results. If we keep the architecture, but increase the number of filters in the network’s highest layer to 512, then the test error decreases to 3.87%, which is not far away from NAS’s best model, whose test error is 3.65%. Impressively, ENAS takes about 7 hours to find this architecture, reducing the number of GPU-hours by more than 50,000x compared to NAS. NASNet-A. There is a growing interest in improving the efficiency of NAS.