Deep invariant networks with differentiable augmentation layers

Understand

Designing learning systems which are invariant to certain data transformations is critical in machine learning.

• Practitioners can typically enforce a desired invariance on the trained model through the choice of a network architecture, e.g.
• using convolutions for translations, or using data augmentation.
• Yet, enforcing true invariance in the network can be difficult, and data invariances are not always known a piori.