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Found 8 papers, 5 papers with code
StyleSwin: Transformer-based GAN for High-resolution Image Generation
To this end, we believe that local attention is crucial to strike the balance between computational efficiency and modeling capacity.
Ranked #1 on
Image Generation
on CelebA-HQ 1024x1024
Vector Quantized Diffusion Model for Text-to-Image Synthesis
Our experiments indicate that the VQ-Diffusion model with the reparameterization is fifteen times faster than traditional AR methods while achieving a better image quality.
Ranked #1 on
Text-to-Image Generation
on Oxford 102 Flowers
(using extra training data)
High-Fidelity and Arbitrary Face Editing
However, we observe that the generator tends to find a tricky way to hide information from the original image to satisfy the constraint of cycle consistency, making it impossible to maintain the rich details (e. g., wrinkles and moles) of non-editing areas.
Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition
A key challenge in video enhancement and action recognition is to fuse useful information from neighboring frames.
PriorGAN: Real Data Prior for Generative Adversarial Nets
To address these two issues, we propose a novel prior that captures the whole real data distribution for GANs, which are called PriorGANs.
GIQA: Generated Image Quality Assessment
Generative adversarial networks (GANs) have achieved impressive results today, but not all generated images are perfect.
Mask-Guided Portrait Editing with Conditional GANs
Portrait editing is a popular subject in photo manipulation.
Arbitrary Style Transfer with Deep Feature Reshuffle
We theoretically prove that our new style loss based on reshuffle connects both global and local style losses respectively used by most parametric and non-parametric neural style transfer methods.
