Large Scale Image Completion via Co-Modulated Generative Adversarial Networks

Anonymous

Keywords: GAN, co-modulation, image completion, image-to-image translation

Abstract: Co-modulated GANs link image-conditional GANs and unconditional modulated models to address large-scale image completion tasks. Co-modulation brings stochastic and conditional style representations together. To improve existing metrics for image completion, the proposed Paired/Unpaired Inception Discriminative Score (P-IDS/U-IDS) is robust to sampling size, captures subtle differences well, and correlates with human preferences. Experiments using co-modulated GANs lead to high quality and diverse results in free-form image completion and image-to-image translation tasks. We extend the findings by Zhao et al. by performing new image completion experiments to examine the biases of co-modulated GANs.

ICLR Paper: https://openreview.net/pdf?id=sSjqmfsk95O

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2103.10428/code)