Review for NeurIPS paper: RandAugment: Practical Automated Data Augmentation with a Reduced Search Space

Weaknesses: The paper only shows proxy task complicated search space may not work as well as using a simple search task without much approximation. It doesn't really tell us what happens if a complicated search space can be efficiently explored on the real task. In this sense, this paper is only a reflection of current practice, without providing a clear direction forward. In fact, the simplification of this paper (reducing the search space to number of op to apply, and the shared magnitude of ops) seems like an over-kill. By doing that, it misses an opportunity to answer some interesting question, such as: "Does assigning a different magnitude to different ops useful at all in auto data augmentation"?