  In this paper, we propose a novel regularization method for Generative
Adversarial Networks, which allows the model to learn discriminative yet
compact binary representations of image patches (image descriptors). We employ
the dimensionality reduction that takes place in the intermediate layers of the
discriminator network and train binarized low-dimensional representation of the
penultimate layer to mimic the distribution of the higher-dimensional preceding
layers. To achieve this, we introduce two loss terms that aim at: (i) reducing
the correlation between the dimensions of the binarized low-dimensional
representation of the penultimate layer i. e. maximizing joint entropy) and
(ii) propagating the relations between the dimensions in the high-dimensional
space to the low-dimensional space. We evaluate the resulting binary image
descriptors on two challenging applications, image matching and retrieval, and
achieve state-of-the-art results.
