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Despite the fast development of differentiable architecture search (DARTS),it suffers from long-standing performance instability, which extremely limitsits application. Existing robustifying methods draw clues from the resultingdeteriorated behavior instead of finding out its causing factor. Variousindicators such as Hessian eigenvalues are proposed as a signal to stopsearching before the performance collapses. However, these indicator-basedmethods tend to easily reject good architectures if the thresholds areinappropriately set, let alone the searching is intrinsically noisy. In thispaper, we undertake a more subtle and direct approach to resolve the collapse.We first demonstrate that skip connections have a clear advantage over othercandidate operations, where it can easily recover from a disadvantageous stateand become dominant. We conjecture that this privilege is causing degeneratedperformance. Therefore, we propose to factor out this benefit with an auxiliaryskip connection, ensuring a fairer competition for all operations. We call thisapproach DARTS-. Extensive experiments on various datasets verify that it cansubstantially improve robustness. Our code is available athttps://github.com/Meituan-AutoML/DARTS- .

DARTS-: Robustly Stepping out of Performance Collapse Without Indicators