  In multimodal unsupervised image-to-image translation tasks, the goal is to
translate an image from the source domain to many images in the target domain.
We present a simple method that produces higher quality images than current
state-of-the-art while maintaining the same amount of multimodal diversity.
Previous methods follow the unconditional approach of trying to map the latent
code directly to a full-size image. This leads to complicated network
architectures with several introduced hyperparameters to tune. By treating the
latent code as a modifier of the convolutional filters, we produce multimodal
output while maintaining the traditional Generative Adversarial Network (GAN)
loss and without additional hyperparameters. The only tuning required by our
method controls the tradeoff between variability and quality of generated
images. Furthermore, we achieve disentanglement between source domain content
and target domain style for free as a by-product of our formulation. We perform
qualitative and quantitative experiments showing the advantages of our method
compared with the state-of-the art on multiple benchmark image-to-image
translation datasets.
