Curriculum-NAS: Curriculum Weight-Sharing Neural Architecture Search

Neural Architecture Search (NAS) is an effective way to automatically design neural architectures for various multimedia applications. Weight-sharing, as one of the most popular NAS strategies, has been widely adopted due to its search efficiency. Existing weight-sharing NAS methods overlook the influence of data distribution and treat each data sample equally. Contrastively, in this paper, we empirically discover that different data samples have different influences on architectures, e.g., some data samples are easy to fit by certain architectures but hard by others. Hence, there exist architectures with better performances on early data samples being more likely to be discovered in the whole NAS searching process, which leads to a suboptimal searching result. To tackle this problem, we propose Curriculum-NAS, a curriculum training framework on weight-sharing NAS, which dynamically changes the training data weights during the searching process. In particular, Curriculum-NAS utilizes the multiple subnets included in weight-sharing NAS to jointly assess data uncertainty, which serves as the difficulty criterion in a curriculum manner, so that the potentially optimal architectures can obtain higher probability of being fully trained and discovered. Extensive experiments on several image and text datasets demonstrate that our Curriculum-NAS can bring consistent improvement over existing weight-sharing NAS. The code is available online at https://github.com/zhouyw16/curriculum-nas.