Search Results for author:
Found 25 papers, 19 papers with code
Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models
We show that guidance is clearly harmful toward the beginning of the chain (high noise levels), largely unnecessary toward the end (low noise levels), and only beneficial in the middle.
Analyzing and Improving the Training Dynamics of Diffusion Models
Diffusion models currently dominate the field of data-driven image synthesis with their unparalleled scaling to large datasets.
Ranked #1 on
Image Generation
on ImageNet 512x512
Generative Novel View Synthesis with 3D-Aware Diffusion Models
We present a diffusion-based model for 3D-aware generative novel view synthesis from as few as a single input image.
StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis
Text-to-image synthesis has recently seen significant progress thanks to large pretrained language models, large-scale training data, and the introduction of scalable model families such as diffusion and autoregressive models.
Ranked #19 on
Text-to-Image Generation
on MS COCO
Simulator-Based Self-Supervision for Learned 3D Tomography Reconstruction
We propose a deep learning method for 3D volumetric reconstruction in low-dose helical cone-beam computed tomography.
eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
Therefore, in contrast to existing works, we propose to train an ensemble of text-to-image diffusion models specialized for different synthesis stages.
Ranked #14 on
Text-to-Image Generation
on MS COCO
Generating Long Videos of Dynamic Scenes
Existing video generation methods often fail to produce new content as a function of time while maintaining consistencies expected in real environments, such as plausible dynamics and object persistence.
Elucidating the Design Space of Diffusion-Based Generative Models
We argue that the theory and practice of diffusion-based generative models are currently unnecessarily convoluted and seek to remedy the situation by presenting a design space that clearly separates the concrete design choices.
The Role of ImageNet Classes in Fréchet Inception Distance
Fr\'echet Inception Distance (FID) is the primary metric for ranking models in data-driven generative modeling.
Efficient Geometry-aware 3D Generative Adversarial Networks
Unsupervised generation of high-quality multi-view-consistent images and 3D shapes using only collections of single-view 2D photographs has been a long-standing challenge.
Alias-Free Generative Adversarial Networks
We observe that despite their hierarchical convolutional nature, the synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner.
Ranked #1 on
Image Generation
on FFHQ-U
Modular Primitives for High-Performance Differentiable Rendering
We present a modular differentiable renderer design that yields performance superior to previous methods by leveraging existing, highly optimized hardware graphics pipelines.
Training Generative Adversarial Networks with Limited Data
We also find that the widely used CIFAR-10 is, in fact, a limited data benchmark, and improve the record FID from 5. 59 to 2. 42.
Ranked #1 on
Conditional Image Generation
on ArtBench-10 (32x32)
Semi-Supervised StyleGAN for Disentanglement Learning
Disentanglement learning is crucial for obtaining disentangled representations and controllable generation.
Analyzing and Improving the Image Quality of StyleGAN
Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.
Ranked #1 on
Image Generation
on LSUN Car 256 x 256
Disentangled GANs for Controllable Generation of High-Resolution Images
Generative adversarial networks (GANs) have achieved great success at generating realistic samples.
Few-Shot Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images.
Improved Precision and Recall Metric for Assessing Generative Models
The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research.
High-Quality Self-Supervised Deep Image Denoising
We describe a novel method for training high-quality image denoising models based on unorganized collections of corrupted images.
A Style-Based Generator Architecture for Generative Adversarial Networks
We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature.
Ranked #1 on
Image Generation
on LSUN Bedroom
Noise2Noise: Learning Image Restoration without Clean Data
We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption.
Progressive Growing of GANs for Improved Quality, Stability, and Variation
We describe a new training methodology for generative adversarial networks.
Ranked #4 on
Image Generation
on LSUN Horse 256 x 256
(Clean-FID (trainfull) metric)
Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion
Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone.
Pruning Convolutional Neural Networks for Resource Efficient Inference
We propose a new criterion based on Taylor expansion that approximates the change in the cost function induced by pruning network parameters.
Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks
We present a real-time deep learning framework for video-based facial performance capture -- the dense 3D tracking of an actor's face given a monocular video.
