630 citations · 1.2k across the 8 of their papers we have counts for
20 papers · 1 filter
Generalizing Dataset Distillation via Deep Generative Prior
George Cazenavette, Tongzhou Wang, Antonio Torralba +2
Dataset Distillation aims to distill an entire dataset's knowledge into a few synthetic images. The idea is to synthesize a small number of synthetic data points that, when given t…
Dataset Distillation by Matching Training Trajectories
George Cazenavette, Tongzhou Wang, Antonio Torralba +2
Dataset distillation is the task of synthesizing a small dataset such that a model trained on the synthetic set will match the test accuracy of the model trained on the full datase…
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…
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…
Rewriting a Deep Generative Model
David Bau, Steven Liu, Tongzhou Wang +2
A deep generative model such as a GAN learns to model a rich set of semantic and physical rules about the target distribution, but up to now, it has been obscure how such rules are…
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…