Title: Parametrizing filters of a CNN with a GAN,
Abstract: It is commonly agreed that the use of relevant invariances as a good
statistical bias is important in machine-learning. However, most approaches
that explicitly incorporate invariances into a model architecture only make use
of very simple transformations, such as translations and rotations. Hence,
there is a need for methods to model and extract richer transformations that
capture much higher-level invariances. To that end, we introduce a tool
allowing to parametrize the set of filters of a trained convolutional neural
network with the latent space of a generative adversarial network. We then show
that the method can capture highly non-linear invariances of the data by
visualizing their effect in the data space.