  Automatic neural architecture search techniques are becoming increasingly
important in machine learning area. Especially, weight sharing methods have
shown remarkable potentials on searching good network architectures with few
computational resources. However, existing weight sharing methods mainly suffer
limitations on searching strategies: these methods either uniformly train all
network paths to convergence which introduces conflicts between branches and
wastes a large amount of computation on unpromising candidates, or selectively
train branches with different frequency which leads to unfair evaluation and
comparison among paths. To address these issues, we propose a novel neural
architecture search method with balanced training strategy to ensure fair
comparisons and a selective drop mechanism to reduce conflicts among candidate
paths. The experimental results show that our proposed method can achieve a
leading performance of 79.0% on ImageNet under mobile settings, which
outperforms other state-of-the-art methods in both accuracy and efficiency.
