论文标题
盆景网络：通过可区分的修剪器进行一次性神经架构搜索
Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Pruners
论文作者
论文摘要
单发神经架构搜索（NAS）旨在最大程度地减少发现最先进模型的计算费用。但是，在过去的一年中，人们注意到幼稚随机搜索在领先的NAS算法使用的相同搜索空间上的可比性能。为了解决这个问题，我们探索了大幅放松NAS搜索空间的效果，并提出了盆景网络，这是一种有效的单发NAS方法，可以探索我们放松的搜索空间。盆景网络围绕修改后的差异培养器构建，并且可以始终如一地发现最先进的体系结构，这些架构比随机搜索要比随机搜索要比其他最新方法更少的参数要少。此外，盆景网络同时进行模型搜索和训练，从而大大减少了从头开始生成全面训练模型所需的总时间。
One-shot Neural Architecture Search (NAS) aims to minimize the computational expense of discovering state-of-the-art models. However, in the past year attention has been drawn to the comparable performance of naive random search across the same search spaces used by leading NAS algorithms. To address this, we explore the effects of drastically relaxing the NAS search space, and we present Bonsai-Net, an efficient one-shot NAS method to explore our relaxed search space. Bonsai-Net is built around a modified differential pruner and can consistently discover state-of-the-art architectures that are significantly better than random search with fewer parameters than other state-of-the-art methods. Additionally, Bonsai-Net performs simultaneous model search and training, dramatically reducing the total time it takes to generate fully-trained models from scratch.
