  In this paper, we propose an inverse reinforcement learning method for
architecture search (IRLAS), which trains an agent to learn to search network
structures that are topologically inspired by human-designed network. Most
existing architecture search approaches totally neglect the topological
characteristics of architectures, which results in complicated architecture
with a high inference latency. Motivated by the fact that human-designed
networks are elegant in topology with a fast inference speed, we propose a
mirror stimuli function inspired by biological cognition theory to extract the
abstract topological knowledge of an expert human-design network (ResNeXt). To
avoid raising a too strong prior over the search space, we introduce inverse
reinforcement learning to train the mirror stimuli function and exploit it as a
heuristic guidance for architecture search, easily generalized to different
architecture search algorithms. On CIFAR-10, the best architecture searched by
our proposed IRLAS achieves 2.60% error rate. For ImageNet mobile setting, our
model achieves a state-of-the-art top-1 accuracy 75.28%, while being 2~4x
faster than most auto-generated architectures. A fast version of this model
achieves 10% faster than MobileNetV2, while maintaining a higher accuracy.
