Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search Space

Random-sampling Neural Architecture Search (RandomNAS) has recently become a prevailing NAS approach because of its search efficiency and simplicity. There are two main steps in RandomNAS: the training step that randomly samples the weight-sharing architectures from a supernet and iteratively updates their weights, and the search step that ranks architectures by their respective validation performance. Key to both steps is the assumption of a high correlation between estimated performance(i.e., accuracy) for weight-sharing architectures and their respective achievable accuracy (i.e., ground truth) when trained from scratch. We examine such a phenomenon via NASBench-201, whose ground truth is known for its entire NAS search space. We observe that existing RandomNAS can rank a set of architectures uniformly sampled from the entire global search space(GS), that correlates well with its ground-truth ranking. However, if we only focus on the top-performing architectures (such as top 20\% according to the ground truth) in the GS, such a correlation drops dramatically. This raises the question of whether we can find an effective proxy search space (PS) that is only a small subset of GS to dramatically improve RandomNAS’s search efficiency while at the same time keeping a good correlation for the top-performing architectures. This paper proposes a new RandomNAS-based approach called EPS (Evolving the Proxy Search Space) to address this problem. We show that, when applied to NASBench-201, EPS can achieve near-optimal NAS performance and beat all existing state-of-the-art. When applied to different-variants of DARTS-like search spaces for tasks such as image classification and natural language processing, EPS is able to robustly achieve superior performance with shorter or similar search time compared to some leading NAS works. The code is available at https://github.com/IcLr2020SuBmIsSiOn/EPS