abstract
convnets, through their architecture, only enforce invariance to translation. in this paper, we
introduce a new class of deep convolutional architectures called non-parametric transformation networks (nptns) which can learn general
invariances and symmetries directly from data.
nptns are a natural generalization of convnets
and can be optimized directly using gradient descent. unlike almost all previous works in deep
architectures, they make no assumption regarding
the structure of the invariances present in the data
and in that aspect are flexible and powerful. we
also model convnets and nptns under a unified framework called transformation networks
(tn), which yields a better understanding of the
connection between the two. we demonstrate the
efficacy of nptns on data such as mnist and
cifar10 where they outperform convnet baselines with the same number of parameters. we
show it is more effective than convnets in modelling symmetries from data, without the explicit
knowledge of the added arbitrary nuisance transformations. finally, we replace convnets with
nptns within capsule networks and show that
this enables capsule nets to perform even better.