  Neural architecture search (NAS) is proposed to automate the architecture
design process and attracts overwhelming interest from both academia and
industry. However, it is confronted with overfitting issue due to the
high-dimensional search space composed by operator selection and skip
connection of each layer. This paper explores the architecture overfitting
issue in depth based on the reinforcement learning-based NAS framework. We show
that the policy gradient method has deep correlations with the cross entropy
minimization. Based on this correlation, we further demonstrate that, though
the reward of NAS is sparse, the policy gradient method implicitly assign the
reward to all operations and skip connections based on the sampling frequency.
However, due to the inaccurate reward estimation, curse of dimensionality
problem and the hierachical structure of neural networks, reward charateristics
for operators and skip connections have intrinsic differences, the assigned
rewards for the skip connections are extremely noisy and inaccurate. To
alleviate this problem, we propose a neural architecture refinement approach
that working with an initial state-of-the-art network structure and only
refining its operators. Extensive experiments have demonstrated that the
proposed method can achieve fascinated results, including classification, face
recognition etc.
