We introduce a novel approach to training generative adversarial networks (GANs, Goodfellow et al., 2014) , where we train a generator to match a target distribution that converges to the data distribution at the limit of a perfect discriminator. This objective can be interpreted as training a generator to produce samples that lie on the decision boundary of a current discriminator in training at each update, and we call a GAN trained using this algorithm a boundary-seeking GAN (BGAN). This approach can be used to train a generator with discrete output when the generator outputs a parametric conditional distribution. We demonstrate the effectiveness of the proposed algorithm with discrete image data. In contrary to the proposed algorithm, we observe that the recently proposed Gumbel-Softmax technique for re-parametrizing the discrete variables does not work for training a GAN with discrete data. Finally, we notice that the proposed boundaryseeking algorithm works even with continuous variables, and demonstrate its effectiveness with two widely used image data sets, SVHN and CelebA.
The boundary-seeking GAN (BGAN) REF aims at generating samples that lie on the decision boundary of a current discriminator in training at each update.
Boundary-Seeking Generative Adversarial Networks
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