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Efficient Neural Architecture Search methods based on weight sharing haveshown good promise in democratizing Neural Architecture Search for computervision models. There is, however, an ongoing debate whether these efficientmethods are significantly better than random search. Here we perform a thoroughcomparison between efficient and random search methods on a family ofprogressively larger and more challenging search spaces for imageclassification and detection on ImageNet and COCO. While the efficacies of bothmethods are problem-dependent, our experiments demonstrate that there arelarge, realistic tasks where efficient search methods can provide substantialgains over random search. In addition, we propose and evaluate techniques whichimprove the quality of searched architectures and reduce the need for manualhyper-parameter tuning.  Source code and experiment data are available athttps://github.com/google-research/google-research/tree/master/tunas

Can weight sharing outperform random architecture search? An investigation with TuNAS