  Neural architecture search (NAS) has become an important approach to
automatically find effective architectures. To cover all possible good
architectures, we need to search in an extremely large search space with
billions of candidate architectures. More critically, given a large search
space, we may face a very challenging issue of space explosion. However, due to
the limitation of computational resources, we can only sample a very small
proportion of the architectures, which provides insufficient information for
the training. As a result, existing methods may often produce suboptimal
architectures. To alleviate this issue, we propose a curriculum search method
that starts from a small search space and gradually incorporates the learned
knowledge to guide the search in a large space. With the proposed search
strategy, our Curriculum Neural Architecture Search (CNAS) method significantly
improves the search efficiency and finds better architectures than existing NAS
methods. Extensive experiments on CIFAR-10 and ImageNet demonstrate the
effectiveness of the proposed method.
