SLACK: Stable Learning of Augmentations with Cold-start and KL regularization
This addresses the need for more automated and flexible data augmentation in machine learning, though it is incremental as it builds on existing bilevel optimization methods.
The paper tackles the problem of learning data augmentation policies without relying on manually-selected transformations, achieving competitive results on standard benchmarks and generalizing beyond natural images.
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.