We propose a new method for learning the structure of convolutional neural
networks (CNNs) that is more efficient than recent state-of-the-art methods
based on reinforcement learning and evolutionary algorithms. Our approach uses
a sequential model-based optimization (SMBO) strategy, in which we search for
structures in order of increasing complexity, while simultaneously learning a
surrogate model to guide the search through structure space. Direct comparison
under the same search space shows that our method is up to 5 times more
efficient than the RL method of Zoph et al. (2018) in terms of number of models
evaluated, and 8 times faster in terms of total compute. The structures we
discover in this way achieve state of the art classification accuracies on
CIFAR-10 and ImageNet.Comment: To appear in ECCV 2018 as oral. The code and checkpoint for PNASNet-5
trained on ImageNet (both Mobile and Large) can now be downloaded from
https://github.com/tensorflow/models/tree/master/research/slim#Pretrained.
Also see https://github.com/chenxi116/PNASNet.TF for refactored and
simplified TensorFlow code; see https://github.com/chenxi116/PNASNet.pytorch
for exact conversion to PyTorc