Tongzhou Wang | DeepAI

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∙ 05/02/2023

Generalizing Dataset Distillation via Deep Generative Prior

Dataset Distillation aims to distill an entire dataset's knowledge into ...

0 George Cazenavette, et al. ∙

∙ 04/03/2023

Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning

In goal-reaching reinforcement learning (RL), the optimal value function...

0 Tongzhou Wang, et al. ∙

∙ 03/25/2023

Deep Augmentation: Enhancing Self-Supervised Learning through Transformations in Higher Activation Space

We introduce Deep Augmentation, an approach to data augmentation using d...

0 Rickard Brüel Gabrielsson, et al. ∙

∙ 02/22/2023

Steerable Equivariant Representation Learning

Pre-trained deep image representations are useful for post-training task...

0 Sangnie Bhardwaj, et al. ∙

∙ 11/29/2022

Procedural Image Programs for Representation Learning

Learning image representations using synthetic data allows training neur...

0 Manel Baradad, et al. ∙

∙ 11/28/2022

Improved Representation of Asymmetrical Distances with Interval Quasimetric Embeddings

Asymmetrical distance structures (quasimetrics) are ubiquitous in our li...

0 Tongzhou Wang, et al. ∙

∙ 09/26/2022

Totems: Physical Objects for Verifying Visual Integrity

We introduce a new approach to image forensics: placing physical refract...

4 Jingwei Ma, et al. ∙

∙ 06/30/2022

On the Learning and Learnablity of Quasimetrics

Our world is full of asymmetries. Gravity and wind can make reaching a p...

0 Tongzhou Wang, et al. ∙

∙ 06/30/2022

Denoised MDPs: Learning World Models Better Than the World Itself

The ability to separate signal from noise, and reason with clean abstrac...

8 Tongzhou Wang, et al. ∙

∙ 03/22/2022

Dataset Distillation by Matching Training Trajectories

Dataset distillation is the task of synthesizing a small dataset such th...

16 George Cazenavette, et al. ∙

∙ 06/10/2021

Learning to See by Looking at Noise

Current vision systems are trained on huge datasets, and these datasets ...

0 Manel Baradad, et al. ∙

∙ 07/30/2020

Rewriting a Deep Generative Model

A deep generative model such as a GAN learns to model a rich set of sema...

12 David Bau, et al. ∙

∙ 06/18/2020

Diverse Image Generation via Self-Conditioned GANs

We introduce a simple but effective unsupervised method for generating r...

12 Steven Liu, et al. ∙

∙ 05/20/2020

Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere

Contrastive representation learning has been outstandingly successful in...

12 Tongzhou Wang, et al. ∙

∙ 11/27/2018

Dataset Distillation

Model distillation aims to distill the knowledge of a complex model into...

3 Tongzhou Wang, et al. ∙

∙ 08/21/2017

Neural Block Sampling

Efficient Monte Carlo inference often requires manual construction of mo...

0 Tongzhou Wang, et al. ∙

∙ 08/10/2017

Learning to Synthesize a 4D RGBD Light Field from a Single Image

We present a machine learning algorithm that takes as input a 2D RGB ima...

0 Pratul P. Srinivasan, et al. ∙

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