  In this paper, we investigate the application of deep convolutional GANs on
car image generation. We improve upon the commonly used DCGAN architecture by
implementing Wasserstein loss to decrease mode collapse and introducing dropout
at the end of the discrimiantor to introduce stochasticity. Furthermore, we
introduce convolutional layers at the end of the generator to improve
expressiveness and smooth noise. All of these improvements upon the DCGAN
architecture comprise our proposal of the novel BoolGAN architecture, which is
able to decrease the FID from 195.922 (baseline) to 165.966.
