Web: http://arxiv.org/abs/2202.02142

Sept. 16, 2022, 1:12 a.m. | Cédric Rommel, Thomas Moreau, Alexandre Gramfort

cs.LG updates on arXiv.org arxiv.org

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. State-of-the-art methods for
learning data augmentation policies require held-out data and are based on
bilevel optimization problems, which are …

arxiv augmentation networks

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