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Zero-cost proxies (ZC proxies) are a recent architecture performanceprediction technique aiming to significantly speed up algorithms for neuralarchitecture search (NAS). Recent work has shown that these techniques showgreat promise, but certain aspects, such as evaluating and exploiting theircomplementary strengths, are under-studied. In this work, we createNAS-Bench-Suite: we evaluate 13 ZC proxies across 28 tasks, creating by far thelargest dataset (and unified codebase) for ZC proxies, enablingorders-of-magnitude faster experiments on ZC proxies, while avoidingconfounding factors stemming from different implementations. To demonstrate theusefulness of NAS-Bench-Suite, we run a large-scale analysis of ZC proxies,including a bias analysis, and the first information-theoretic analysis whichconcludes that ZC proxies capture substantial complementary information.Motivated by these findings, we present a procedure to improve the performanceof ZC proxies by reducing biases such as cell size, and we also show thatincorporating all 13 ZC proxies into the surrogate models used by NASalgorithms can improve their predictive performance by up to 42%. Our code anddatasets are available at https://github.com/automl/naslib/tree/zerocost.

NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies