2.3k citations · 6.3k across the 61 of their papers we have counts for
12 papers · 1 filter
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…
Improved Representation of Asymmetrical Distances with Interval Quasimetric Embeddings
Tongzhou Wang, Phillip Isola
Asymmetrical distance structures (quasimetrics) are ubiquitous in our lives and are gaining more attention in machine learning applications. Imposing such quasimetric structures in…
Powderworld: A Platform for Understanding Generalization via Rich Task Distributions
Kevin Frans, Phillip Isola
One of the grand challenges of reinforcement learning is the ability to generalize to new tasks. However, general agents require a set of rich, diverse tasks to train on. Designing…
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…
Developing a Series of AI Challenges for the United States Department of the Air Force
Vijay Gadepally, Gregory Angelides, Andrei Barbu +39
Through a series of federal initiatives and orders, the U.S. Government has been making a concerted effort to ensure American leadership in AI. These broad strategy documents have…
Semantic uncertainty intervals for disentangled latent spaces
Swami Sankaranarayanan, Anastasios N. Angelopoulos, Stephen Bates +2
Meaningful uncertainty quantification in computer vision requires reasoning about semantic information -- say, the hair color of the person in a photo or the location of a car on t…
On the Learning and Learnability of Quasimetrics
Tongzhou Wang, Phillip Isola
Our world is full of asymmetries. Gravity and wind can make reaching a place easier than coming back. Social artifacts such as genealogy charts and citation graphs are inherently d…
Denoised MDPs: Learning World Models Better Than the World Itself
Tongzhou Wang, Simon S. Du, Antonio Torralba +3
The ability to separate signal from noise, and reason with clean abstractions, is critical to intelligence. With this ability, humans can efficiently perform real world tasks witho…
NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields
Lin Yen-Chen, Pete Florence, Jonathan T. Barron +3
Thin, reflective objects such as forks and whisks are common in our daily lives, but they are particularly challenging for robot perception because it is hard to reconstruct them u…
Any-resolution Training for High-resolution Image Synthesis
Lucy Chai, Michael Gharbi, Eli Shechtman +2
Generative models operate at fixed resolution, even though natural images come in a variety of sizes. As high-resolution details are downsampled away and low-resolution images are…
Learning to generate line drawings that convey geometry and semantics
Caroline Chan, Fredo Durand, Phillip Isola
This paper presents an unpaired method for creating line drawings from photographs. Current methods often rely on high quality paired datasets to generate line drawings. However, t…
Exploring Visual Prompts for Adapting Large-Scale Models
Hyojin Bahng, Ali Jahanian, Swami Sankaranarayanan +1
We investigate the efficacy of visual prompting to adapt large-scale models in vision. Following the recent approach from prompt tuning and adversarial reprogramming, we learn a si…