Population Based Augmentation

Population Based Augmentation, or PBA, is a data augmentation strategy (PBA), which generates nonstationary augmentation policy schedules instead of a fixed augmentation policy. In PBA we consider the augmentation policy search problem as a special case of hyperparameter schedule learning. It leverages Population Based Training (PBT), a hyperparameter search algorithm which optimizes the parameters of a network jointly with their hyperparameters to maximize performance. The output of PBT is not an optimal hyperparameter configuration but rather a trained model and schedule of hyperparameters.

In PBA, we are only interested in the learned schedule and discard the child model result (similar to AutoAugment). This learned augmentation schedule can then be used to improve the training of different (i.e., larger and costlier to train) models on the same dataset.

PBT executes as follows. To start, a fixed population of models are randomly initialized and trained in parallel. At certain intervals, an “exploit-and-explore” procedure is applied to the worse performing population members, where the model clones the weights of a better performing model (i.e., exploitation) and then perturbs the hyperparameters of the cloned model to search in the hyperparameter space (i.e., exploration). Because the weights of the models are cloned and never reinitialized, the total computation required is the computation to train a single model times the population size.