  Efficient Neural Architecture Search methods based on weight sharing have
shown good promise in democratizing Neural Architecture Search for computer
vision models. There is, however, an ongoing debate whether these efficient
methods are significantly better than random search. Here we perform a thorough
comparison between efficient and random search methods on a family of
progressively larger and more challenging search spaces for image
classification and detection on ImageNet and COCO. While the efficacies of both
methods are problem-dependent, our experiments demonstrate that there are
large, realistic tasks where efficient search methods can provide substantial
gains over random search. In addition, we propose and evaluate techniques which
improve the quality of searched architectures and reduce the need for manual
hyper-parameter tuning.
  Source code and experiment data are available at
https://github.com/google-research/google-research/tree/master/tunas

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning