IRLAS: Inverse Reinforcement Learning for Architecture Search

CVPR 2019 Minghao GuoZhao ZhongWei WuDahua LinJunjie Yan

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... (read more)

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