Abstract
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.
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