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.
This paper introduces a better searching strategy in the context of automatic neural architecture search (NAS). Especially, they focus on improving the search strategy for previously proposed computationally effective weight sharing methods for NAS. Current search strategies for the weight sharing NAS methods either focus on uniformly training all the network paths or selectively train different network paths with different frequency, where both have their own issues like wasting resources for unpromising candidates and unfair comparison among network paths. To this end, this paper proposes a balanced training strategy with “selective drop mechanism”. Further, they validate their approach by showing leading performance on ImageNet under mobile settings.
Overall, I appreciate the effort on exploring new strategies for better search algorithm for NAS in the context of weight sharing methods. However, the analysis of the current approach is limited and some relevant papers are missing. More details below. 
Arguments:
1) This paper’s main focus is on developing a better search strategy for NAS based weight sharing methods. The validation of their approach is supported via showing improvement in the accuracy on ImageNet task. However, my main argument is that you have to validate the proposed approach with better analysis on the search space along with improvement on the tasks. For example, “Evaluating the Search Phase of Neural Architecture Search” paper which studies the effectiveness of current search strategies is not referenced in this paper. I would suggest to do analysis of you search strategy by following the analysis experiments conducted in the above paper. 
2) The related work doesn’t discuss the latest papers which suggest why and in what ways the current search strategies are based for weight-sharing based NAS approaches. As this paper is trying to address this problem, I would naturally assume that this is well discussed, but it’s missing!