AutoAugment

• developed by Cubuk et al.
• much different approach to meta-learning than Neural Augmentation
• AutoAugment is a Reinforcement Learning algorithm that searches for an optimal augmentation policy amongst a constrained set of Geometric Transformations with miscellaneous levels of distortions. For example, ‘translateX 20 pixels’ could be one of the transformations in the search space
• In Reinforcement Learning algorithms, a policy is analogous to the strategy of the learning algorithm. This policy determines what actions to take at given states to achieve some goal. The AutoAugment approach learns a policy which consists of many subpolicies, each sub-policy consisting of an image transformation and a magnitude of transformation
• Reinforcement Learning is thus used as a discrete search algorithm of augmentations.