  Data augmentation is known to improve the generalization capabilities of
neural networks, provided that the set of transformations is chosen with care,
a selection often performed manually. Automatic data augmentation aims at
automating this process. However, most recent approaches still rely on some
prior information; they start from a small pool of manually-selected default
transformations that are either used to pretrain the network or forced to be
part of the policy learned by the automatic data augmentation algorithm. In
this paper, we propose to directly learn the augmentation policy without
leveraging such prior knowledge. The resulting bilevel optimization problem
becomes more challenging due to the larger search space and the inherent
instability of bilevel optimization algorithms. To mitigate these issues (i) we
follow a successive cold-start strategy with a Kullback-Leibler regularization,
and (ii) we parameterize magnitudes as continuous distributions. Our approach
leads to competitive results on standard benchmarks despite a more challenging
setting, and generalizes beyond natural images.
