H-NeXt: The next step towards roto-translation invariant networks
Tomas Karella Filip Sroubek Jan Flusser Jan Blazek Vasek Kosik
The widespread popularity of equivariant networks underscores the
significance of parameter efficient models and effective use of training data.
At a time when robustness to unseen deformations is becoming increasingly
important, we present H-NeXt, which bridges the gap between equivariance and
invariance. H-NeXt is a parameter-efficient roto-translation invariant network
that is trained without a single augmented image in the training set. Our
network comprises three components: an equivariant backbone for learning
roto-translation independent features, an invariant pooling layer for
discarding roto-translation information, and a classification layer. H-NeXt
outperforms the state of the art in classification on unaugmented training sets
and augmented test sets of MNIST and CIFAR-10.