170 citations · 251 across the 13 of their papers we have counts for
8 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…
Steerable Equivariant Representation Learning
Sangnie Bhardwaj, Willie McClinton, Tongzhou Wang +4
Pre-trained deep image representations are useful for post-training tasks such as classification through transfer learning, image retrieval, and object detection. Data augmentation…
Procedural Image Programs for Representation Learning
Manel Baradad, Chun-Fu Chen, Jonas Wulff +4
Learning image representations using synthetic data allows training neural networks without some of the concerns associated with real images, such as privacy and bias. Existing wor…
Totems: Physical Objects for Verifying Visual Integrity
Jingwei Ma, Lucy Chai, Minyoung Huh +4
We introduce a new approach to image forensics: placing physical refractive objects, which we call totems, into a scene so as to protect any photograph taken of that scene. Totems…
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…
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…
Diverse Image Generation via Self-Conditioned GANs
Steven Liu, Tongzhou Wang, David Bau +2
We introduce a simple but effective unsupervised method for generating realistic and diverse images. We train a class-conditional GAN model without using manually annotated class l…
Learning to Synthesize a 4D RGBD Light Field from a Single Image
Pratul P. Srinivasan, Tongzhou Wang, Ashwin Sreelal +2
We present a machine learning algorithm that takes as input a 2D RGB image and synthesizes a 4D RGBD light field (color and depth of the scene in each ray direction). For training,…