BinGAN: Learning Compact Binary Descriptors with a Regularized GAN

TL;DR

This paper introduces BinGAN, a novel method that uses regularized GANs to learn compact, discriminative binary image descriptors, achieving state-of-the-art results in image matching and retrieval tasks.

Contribution

It proposes a new regularization technique for GANs to produce low-dimensional binary descriptors that preserve high-dimensional relations.

Findings

Achieves state-of-the-art performance in image matching.

Produces discriminative binary descriptors.

Effective in image retrieval applications.

Abstract

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.