  Data augmentation is a key element of deep learning pipelines, as it informs
the network during training about transformations of the input data that keep
the label unchanged. Manually finding adequate augmentation methods and
parameters for a given pipeline is however rapidly cumbersome. In particular,
while intuition can guide this decision for images, the design and choice of
augmentation policies remains unclear for more complex types of data, such as
neuroscience signals. Besides, class-dependent augmentation strategies have
been surprisingly unexplored in the literature, although it is quite intuitive:
changing the color of a car image does not change the object class to be
predicted, but doing the same to the picture of an orange does. This paper
investigates gradient-based automatic data augmentation algorithms amenable to
class-wise policies with exponentially larger search spaces. Motivated by
supervised learning applications using EEG signals for which good augmentation
policies are mostly unknown, we propose a new differentiable relaxation of the
problem. In the class-agnostic setting, results show that our new relaxation
leads to optimal performance with faster training than competing gradient-based
methods, while also outperforming gradient-free methods in the class-wise
setting. This work proposes also novel differentiable augmentation operations
relevant for sleep stage classification.
