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BinGAN: Learning Compact Binary Descriptors with a Regularized GAN
[article]
2018
arXiv
pre-print
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
arXiv:1806.06778v5
fatcat:ucfzk2674ranplj6lnsvomxwvm