@InProceedings{YaxingWang2022,
author="Yaxing Wang
and Joost Van de Weijer
and Lu Yu
and Shangling Jui",
title="Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data",
booktitle="10th International Conference on Learning Representations",
year="2022",
abstract="Conditional image synthesis is an integral part of many X2I translation systems, including image-to-image, text-to-image and audio-to-image translation systems. Training these large systems generally requires huge amounts of training data. Therefore, we investigate knowledge distillation to transfer knowledge from a high-quality unconditioned generative model (e.g., StyleGAN) to a conditioned synthetic image generation modules in a variety of systems. To initialize the conditional and reference branch (from a unconditional GAN)  we exploit the style mixing characteristics of high-quality GANs to generate an infinite supply of style-mixed triplets to perform the knowledge distillation. Extensive experimental results in a number of image generation tasks (i.e., image-to-image, semantic segmentation-to-image, text-to-image and audio-to-image) demonstrate qualitatively and quantitatively that our method successfully transfers knowledge to the synthetic image generation modules, resulting in more realistic images than previous methods as confirmed by a significant drop in the FID.",
optnote="LAMP; 600.147",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3791), last updated on Mon, 30 Oct 2023 12:07:36 +0100"
}