Search Results for author:
Found 3 papers, 2 papers with code
Diversity-aware Channel Pruning for StyleGAN Compression
Specifically, by assessing channel importance based on their sensitivities to latent vector perturbations, our method enhances the diversity of samples in the compressed model.
Towards Squeezing-Averse Virtual Try-On via Sequential Deformation
In this paper, we first investigate a visual quality degradation problem observed in recent high-resolution virtual try-on approach.
Local Attention Pyramid for Scene Image Generation
To address this, we propose a novel attention module, Local Attention Pyramid (LAP) module tailored for scene image synthesis, that encourages GANs to generate diverse object classes in a high quality by explicit spread of high attention scores to local regions, since objects in scene images are scattered over the entire images.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
