201 citations · 255 across the 5 of their papers we have counts for
7 papers
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Benjamin Eyre, Elliot Creager, David Madras +2
Designing deep neural network classifiers that perform robustly on distributions differing from the available training data is an active area of machine learning research. However,…
Scalable Diffusion Models with Transformers
William Peebles, Saining Xie
We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a tran…
Learning to Learn with Generative Models of Neural Network Checkpoints
William Peebles, Ilija Radosavovic, Tim Brooks +2
We explore a data-driven approach for learning to optimize neural networks. We construct a dataset of neural network checkpoints and train a generative model on the parameters. In…
GAN-Supervised Dense Visual Alignment
William Peebles, Jun-Yan Zhu, Richard Zhang +3
We propose GAN-Supervised Learning, a framework for learning discriminative models and their GAN-generated training data jointly end-to-end. We apply our framework to the dense vis…
The Hessian Penalty: A Weak Prior for Unsupervised Disentanglement
William Peebles, John Peebles, Jun-Yan Zhu +2
Existing disentanglement methods for deep generative models rely on hand-picked priors and complex encoder-based architectures. In this paper, we propose the Hessian Penalty, a sim…
Semantic Photo Manipulation with a Generative Image Prior
David Bau, Hendrik Strobelt, William Peebles +4
Despite the recent success of GANs in synthesizing images conditioned on inputs such as a user sketch, text, or semantic labels, manipulating the high-level attributes of an existi…
Seeing What a GAN Cannot Generate
David Bau, Jun-Yan Zhu, Jonas Wulff +4
Despite the success of Generative Adversarial Networks (GANs), mode collapse remains a serious issue during GAN training. To date, little work has focused on understanding and quan…