Search Results for author:
Found 25 papers, 22 papers with code
Guiding a Diffusion Model with a Bad Version of Itself
The primary axes of interest in image-generating diffusion models are image quality, the amount of variation in the results, and how well the results align with a given condition, e. g., a class label or a text prompt.
Ranked #1 on
Image Generation
on ImageNet 512x512
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.
Ranked #6 on
Image Generation
on ImageNet 512x512
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 #17 on
Image Generation
on ImageNet 512x512
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.
Solving Audio Inverse Problems with a Diffusion Model
This paper presents CQT-Diff, a data-driven generative audio model that can, once trained, be used for solving various different audio inverse problems in a problem-agnostic setting.
Disentangling Random and Cyclic Effects in Time-Lapse Sequences
We introduce the problem of disentangling time-lapse sequences in a way that allows separate, after-the-fact control of overall trends, cyclic effects, and random effects in the images, and describe a technique based on data-driven generative models that achieves this goal.
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.
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.
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)
GANSpace: Discovering Interpretable GAN Controls
This paper describes a simple technique to analyze Generative Adversarial Networks (GANs) and create interpretable controls for image synthesis, such as change of viewpoint, aging, lighting, and time of day.
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
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
Many machine learning models operate on images, but ignore the fact that images are 2D projections formed by 3D geometry interacting with light, in a process called rendering.
Ranked #4 on
Single-View 3D Reconstruction
on ShapeNet
E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles
It has been recently shown that the hidden variables of convolutional neural networks make for an efficient perceptual similarity metric that accurately predicts human judgment on relative image similarity assessment.
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.
Ranked #4 on
Image Generation
on FFHQ
Improved Self-Supervised Deep Image Denoising
We describe techniques for training high-quality image denoising models that require only single instances of corrupted images as training data.
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.
PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation
Proximal Policy Optimization (PPO) is a highly popular model-free reinforcement learning (RL) approach.
Differentiable Monte Carlo Ray Tracing through Edge Sampling
We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters.
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.
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.
