# Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search Source: [Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:29:00.055Z Authors: Yong Guo, Yaofo Chen, Yin Zheng, Peilin Zhao, Jian Chen, Junzhou Huang, Mingkui Tan Published: 2020-07-07T02:29:06.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on ImageNet (test) (Standard Accuracy)](): Image Classification; ImageNet (test); Top-1 Accuracy: 75.4. ## Original sources and implementation [Original paper]() [PDF]()