RiPPLE：基于早期训练的跨空间性能预测用于神经架构搜索

中文摘要 AI 辅助

神经架构搜索（NAS）评估候选网络，但为了对整个搜索空间进行排序而充分训练足够多的架构是昂贵的。零成本代理在初始化时对架构进行评分，但其排序质量在不同搜索空间之间差异很大。学习型预测器降低了评估成本，但通常需要针对单个候选的完全训练标签或部分训练特征。我们提出了RiPPLE，即基于前缀传播标签外推的排序方法，该方法将部分训练视为一个小的锚点覆盖集的标签来源。RiPPLE将这些锚点训练到早期前缀，将其学习曲线外推为代理标签，并在无标签的架构特征上传播这些标签。早期训练信号仍然是锚点上的标签，而不是每个候选的特征。特征、读出和编码规则无需保留验证集准确率即可选择，并在不同搜索空间之间重用。我们在来自四个搜索空间家族的十二个基准单元以及更大的DARTS空间上评估了该方法。结果考察了排序质量、标签效率、架构选择以及读出、覆盖和传播的作用。RiPPLE通过部分锚点训练预算提供了整个空间的排序，其比较结果在各自的评估和成本协议下进行解释。

英文摘要

Neural architecture search (NAS) evaluates candidate networks, but fully training enough architectures to rank an entire space is expensive. Zero-cost proxies score architectures at initialization, yet their ranking quality varies across search spaces. Learned predictors reduce evaluation cost but typically require fully trained labels or partial-training features for individual candidates. We introduce $\textbf{RiPPLE}$, $\underline{\textbf{R}}$anking v$\underline{\textbf{i}}$a $\underline{\textbf{P}}$refix-$\underline{\textbf{P}}$ropagated $\underline{\textbf{L}}$abel $\underline{\textbf{E}}$xtrapolation, which treats partial training as a source of labels for a small coverage set of anchors. RiPPLE trains these anchors to an early prefix, extrapolates their learning curves to surrogate labels, and propagates the labels over label-free architecture features. The early-training signal remains a label on the anchors rather than a per-candidate feature. Feature, readout, and encoding rules are selected without held-out accuracy and reused across search spaces. We evaluate the method on twelve benchmark cells from four search-space families and on the larger DARTS space. The results examine ranking quality, label efficiency, architecture selection, and the roles of readout, coverage, and propagation. RiPPLE provides a whole-space ranking from a fractional anchor-training budget, with comparisons interpreted under their respective evaluation and cost protocols.

发表机构

• University of New South Wales（新南威尔士大学）
• Korea Advanced Institute of Science and Technology（韩国科学技术院）

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