Autoaugment

AutoAugment is a learned data augmentation strategy that uses reinforcement learning to search for the best augmentation policy — discovering which combinations and magnitudes of image transformations maximize validation accuracy for a given dataset.

How Does AutoAugment Work?

• Search Space: Each policy = 5 sub-policies. Each sub-policy = 2 transformations, each with probability and magnitude.
• Controller: An RNN controller proposes augmentation policies.
• Reward: The policy is evaluated by training a small child model — validation accuracy is the reward.
• Transfer: Policies found on ImageNet transfer well to other datasets.
• Paper: Cubuk et al. (2019, Google Brain).

Why It Matters

• Learned Augmentation: Demonstrated that augmentation strategies can be learned, not just hand-designed.
• Accuracy Boost: +0.4-1.0% on ImageNet, larger gains on smaller datasets (CIFAR-10, SVHN).
• Expensive: The search process requires thousands of GPU hours — motivating RandAugment.

AutoAugment is NAS for data augmentation — using reinforcement learning to discover the optimal augmentation recipe for any dataset.