  We propose the BinaryGAN, a novel generative adversarial network (GAN) that
uses binary neurons at the output layer of the generator. We employ the
sigmoid-adjusted straight-through estimators to estimate the gradients for the
binary neurons and train the whole network by end-to-end backpropogation. The
proposed model is able to directly generate binary-valued predictions at test
time. We implement such a model to generate binarized MNIST digits and
experimentally compare the performance for different types of binary neurons,
GAN objectives and network architectures. Although the results are still
preliminary, we show that it is possible to train a GAN that has binary neurons
and that the use of gradient estimators can be a promising direction for
modeling discrete distributions with GANs. For reproducibility, the source code
is available at https://github.com/salu133445/binarygan .
