Automatic Data Augmentation via Invariance-Constrained Learning

Underlying data structures, such as symmetries or invariance to transformations, are often exploited to improve the solution of learning tasks. However, embedding these properties in models or learning algorithms can be challenging and computationally intensive. Data augmentation, on the other hand, induces these symmetries during training by applying multiple transformations to the input data. Despite its ubiquity, its effectiveness depends on the choices of which transformations to apply, when to do so, and how often. In fact, there is both empirical and theoretical evidence that the indiscriminate use of data augmentation can introduce biases that outweigh its benefits. This work tackles these issues by automatically adapting the data augmentation while solving the learning task. To do so, it formulates data augmentation as an invariance constrained learning problem and leverages Monte Carlo Markov Chain (MCMC) sampling to solve it. The result is an algorithm that not only does away with a priori searches for augmentation distributions, but also dynamically controls if and when data augmentation is applied. We validate empirically our theoretical developments in automatic data augmentation benchmarks for CIFAR and ImageNet-100 datasets. Furthermore, our experiments show how this approach can be used to gather insights on the actual symmetries underlying a learning task.