Learning Invariances in Neural Networks

Understand

Invariances to translations have imbued convolutional neural networks with powerful generalization properties.

• However, we often do not know a priori what invariances are present in the data, or to what extent a model should be invariant to a given symmetry group.
• We show how to \emph{learn} invariances and equivariances by parameterizing a distribution over augmentations and optimizing the training loss simultaneously with respect to the network parameters and augmentation parameters.
• With this simple procedure we can recover the correct set and extent of invariances on image classification, regression, segmentation, and molecular property prediction from a large space of augmentations, on training data alone.