630 citations · 1.1k across the 20 of their papers we have counts for
48 papers
Understanding Collapse in Non-Contrastive Siamese Representation Learning
Alexander C. Li, Alexei A. Efros, Deepak Pathak
Contrastive methods have led a recent surge in the performance of self-supervised representation learning (SSL). Recent methods like BYOL or SimSiam purportedly distill these contr…
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…
Test-Time Training with Masked Autoencoders
Yossi Gandelsman, Yu Sun, Xinlei Chen +1
Test-time training adapts to a new test distribution on the fly by optimizing a model for each test input using self-supervision. In this paper, we use masked autoencoders for this…
Studying Bias in GANs through the Lens of Race
Vongani H. Maluleke, Neerja Thakkar, Tim Brooks +5
In this work, we study how the performance and evaluation of generative image models are impacted by the racial composition of their training datasets. By examining and controlling…
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…
Video Autoencoder: self-supervised disentanglement of static 3D structure and motion
Zihang Lai, Sifei Liu, Alexei A. Efros +1
A video autoencoder is proposed for learning disentan- gled representations of 3D structure and camera pose from videos in a self-supervised manner. Relying on temporal continuity…