2.3k citations · 3.5k across the 13 of their papers we have counts for
14 papers
Correction Training for Diffusion Models
Donghoon Ahn, Runqian Wang, Alexei A. Efros +2
Iterative sampling in diffusion models can lead to error accumulation due to exposure bias. The denoiser is trained on real images interpolated with noise, but at inference its inp…
One-Step Image Translation with Text-to-Image Models
Gaurav Parmar, Taesung Park, Srinivasa Narasimhan +1
In this work, we address two limitations of existing conditional diffusion models: their slow inference speed due to the iterative denoising process and their reliance on paired da…
Holistic Evaluation of Text-To-Image Models
Tony Lee, Michihiro Yasunaga, Chenlin Meng +15
The stunning qualitative improvement of recent text-to-image models has led to their widespread attention and adoption. However, we lack a comprehensive quantitative understanding…
Expressive Text-to-Image Generation with Rich Text
Songwei Ge, Taesung Park, Jun-Yan Zhu +1
Plain text has become a prevalent interface for text-to-image synthesis. However, its limited customization options hinder users from accurately describing desired outputs. For exa…
Scaling up GANs for Text-to-Image Synthesis
Minguk Kang, Jun-Yan Zhu, Richard Zhang +4
The recent success of text-to-image synthesis has taken the world by storm and captured the general public's imagination. From a technical standpoint, it also marked a drastic chan…
Domain Expansion of Image Generators
Yotam Nitzan, Michaël Gharbi, Richard Zhang +4
Can one inject new concepts into an already trained generative model, while respecting its existing structure and knowledge? We propose a new task - domain expansion - to address t…
BlobGAN: Spatially Disentangled Scene Representations
Dave Epstein, Taesung Park, Richard Zhang +2
We propose an unsupervised, mid-level representation for a generative model of scenes. The representation is mid-level in that it is neither per-pixel nor per-image; rather, scenes…
Contrastive Feature Loss for Image Prediction
Alex Andonian, Taesung Park, Bryan Russell +3
Training supervised image synthesis models requires a critic to compare two images: the ground truth to the result. Yet, this basic functionality remains an open problem. A popular…
A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving
Jay Shenoy, Edward Kim, Xiangyu Yue +4
Safely interacting with humans is a significant challenge for autonomous driving. The performance of this interaction depends on machine learning-based modules of an autopilot, suc…
Contrastive Learning for Unpaired Image-to-Image Translation
Taesung Park, Alexei A. Efros, Richard Zhang +1
In image-to-image translation, each patch in the output should reflect the content of the corresponding patch in the input, independent of domain. We propose a straightforward meth…
Swapping Autoencoder for Deep Image Manipulation
Taesung Park, Jun-Yan Zhu, Oliver Wang +4
Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing…
Semantic Image Synthesis with Spatially-Adaptive Normalization
Taesung Park, Ming-Yu Liu, Ting-Chun Wang +1
We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout. Previous methods directly feed the…
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Judy Hoffman, Eric Tzeng, Taesung Park +5
Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are di…
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Jun-Yan Zhu, Taesung Park, Phillip Isola +1
Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of alig…