RandAugment is a simple, automated data augmentation strategy that randomly selects $N$ transformations from a pool and applies them with a fixed magnitude $M$ — eliminating the need for a separate search phase (unlike AutoAugment), with just two hyperparameters.

How Does RandAugment Work?

• Pool: ~14 transformations (rotation, shear, translate, brightness, contrast, equalize, etc.).
• Sample: Randomly pick $N$ transformations (typically $N = 2-3$).
• Apply: Apply each with the same global magnitude $M$ (typically $M = 9-15$ on a 0-30 scale).
• Two Hyperparameters: Only $N$ and $M$ to tune. No separate search phase.
• Paper: Cubuk et al. (2020).

Why It Matters

• Simplicity: Two hyperparameters ($N$, $M$) vs. AutoAugment's expensive policy search.
• Competitive: Matches or exceeds AutoAugment accuracy despite being vastly simpler.
• Standard: The default augmentation strategy in EfficientNet, ViT, FixMatch, and modern training recipes.

RandAugment is augmentation without the search — a dead-simple two-parameter strategy that rivals expensive learned augmentation policies.