Differentiable architecture search (DAS) revolutionizes neural architecture search (NAS) with time-efficient automation, transitioning from discrete candidate sampling and evaluation to differentiable super-net optimization and discretization. However, existing DAS methods either only conduct coarse-grained operation-level search or manually define the remaining ratios for fine-grained kernel-level and weight-level units, which fail to simultaneously optimize model size and model performance. Furthermore, these methods compromise search quality to reduce memory consumption. To tackle these issues, we introduce multi-granularity architecture search (MGAS), a unified framework which aims to comprehensively and memory-efficiently explore the multi-granularity search space to discover both effective and efficient neural networks. Specifically, we learn discretization functions specific to each granularity level to adaptively determine the remaining ratios according to the evolving architecture. This ensures an optimal balance among units of different granularity levels for different target model sizes. Considering the memory demands, we break down the super-net optimization and discretization into multiple sub-net stages. Nevertheless, the greedy nature of this approach may introduce bias in the early stages. To compensate for the bias, we propose progressive re-evaluation to allow for re-pruning and regrowing of previous units during subsequent stages. Extensive experiments on CIFAR-10, CIFAR-100 and ImageNet demonstrate that MGAS outperforms other state-of-the-art methods in achieving a better trade-off between model performance and model size.

翻译：可微架构搜索（DAS）通过从离散候选采样和评估向可微超网络优化与离散化的转变，革新了神经架构搜索（NAS）领域，实现了高效自动化。然而，现有DAS方法要么仅进行粗粒度的操作级搜索，要么人为定义细粒度卷积核级和权重级单元的保留比例，无法同时优化模型大小与模型性能。此外，这些方法为降低内存消耗而牺牲了搜索质量。为解决上述问题，我们提出多粒度架构搜索（MGAS）——一个统一框架，旨在全面且内存高效地探索多粒度搜索空间，以发现兼具高效性与有效性的神经网络。具体而言，我们针对每个粒度层级学习对应的离散化函数，根据动态进化的架构自适应确定保留比例。这确保了针对不同目标模型大小时，不同粒度层级单元之间的最优平衡。考虑到内存需求，我们将超网络优化和离散化分解为多个子网络阶段。然而，这种方法的贪心性质可能导致早期阶段引入偏差。为补偿偏差，我们提出渐进式重评估机制，允许在后续阶段对先前单元进行重新剪枝和增长。在CIFAR-10、CIFAR-100和ImageNet上的大量实验表明，MGAS在模型性能与模型大小之间取得了更优的权衡，优于其他最先进方法。