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Data augmentation is known to improve the generalization capabilities ofneural networks, provided that the set of transformations is chosen with care,a selection often performed manually. Automatic data augmentation aims atautomating this process. However, most recent approaches still rely on someprior information; they start from a small pool of manually-selected defaulttransformations that are either used to pretrain the network or forced to bepart of the policy learned by the automatic data augmentation algorithm. Inthis paper, we propose to directly learn the augmentation policy withoutleveraging such prior knowledge. The resulting bilevel optimization problembecomes more challenging due to the larger search space and the inherentinstability of bilevel optimization algorithms. To mitigate these issues (i) wefollow a successive cold-start strategy with a Kullback-Leibler regularization,and (ii) we parameterize magnitudes as continuous distributions. Our approachleads to competitive results on standard benchmarks despite a more challengingsetting, and generalizes beyond natural images.

SLACK: Stable Learning of Augmentations with Cold-start and KL regularization