# You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization Source: [You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:17:53.140Z Authors: Xinbang Zhang, Zehao Huang, Naiyan Wang Published: 2018-11-05T09:28:13.000Z ## Abstract Recently Neural Architecture Search (NAS) has aroused great interest in both academia and industry, however it remains challenging because of its huge and non-continuous search space. Instead of applying evolutionary algorithm or reinforcement learning as previous works, this paper proposes a Direct Sparse Optimization NAS (DSO-NAS) method. In DSO-NAS, we provide a novel model pruning view to NAS problem. In specific, we start from a completely connected block, and then introduce scaling factors to scale the information flow between operations. Next, we impose sparse regularizations to prune useless connections in the architecture. Lastly, we derive an efficient and theoretically sound optimization method to solve it. Our method enjoys both advantages of differentiability and efficiency, therefore can be directly applied to large datasets like ImageNet. Particularly, On CIFAR-10 dataset, DSO-NAS achieves an average test error 2.84\\%, while on the ImageNet dataset DSO-NAS achieves 25.4\\% test error under 600M FLOPs with 8 GPUs in 18 hours. ## 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. ## Original sources and implementation [Original paper]() [PDF]()