FreeAugment: Data Augmentation Search Across All Degrees of Freedom

Niv Nayman

Abstract:

Data augmentation has become an integral part of deep
learning, as it is known to improve the generalization capabilities of neural networks. Since the most effective set of image transformations differs
between tasks and domains, automatic data augmentation search aims
to alleviate the extreme burden of manually finding the optimal image
transformations. However, current methods are not able to jointly optimize all degrees of freedom: (1) the number of transformations to be
applied, their (2) types, (3) order, and (4) magnitudes. Many existing
methods risk picking the same transformation more than once, limit the
search to two transformations only, or search for the number of transformations exhaustively or iteratively in a myopic manner. Our approach,
FreeAugment, is the first to achieve global optimization of all four degrees of freedom simultaneously, using a fully differentiable method. It
efficiently learns the number of transformations and a probability distribution over their permutations, inherently refraining from redundant
repetition while sampling. Our experiments demonstrate that this joint
learning of all degrees of freedom significantly improves performance,
achieving state-of-the-art results on various natural image benchmarks
and beyond across other domains.