4.3k citations · 10.2k across the 23 of their papers we have counts for
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Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods
Skanda Koppula, Yazhe Li, Evan Shelhamer +5
Self-supervised methods have achieved remarkable success in transfer learning, often achieving the same or better accuracy than supervised pre-training. Most prior work has done so…
Back to the Source: Diffusion-Driven Test-Time Adaptation
Jin Gao, Jialing Zhang, Xihui Liu +3
Test-time adaptation harnesses test inputs to improve the accuracy of a model trained on source data when tested on shifted target data. Existing methods update the source model by…
Object discovery and representation networks
Olivier J. Hénaff, Skanda Koppula, Evan Shelhamer +5
The promise of self-supervised learning (SSL) is to leverage large amounts of unlabeled data to solve complex tasks. While there has been excellent progress with simple, image-leve…
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses
Francesco Croce, Sven Gowal, Thomas Brunner +3
Adaptive defenses, which optimize at test time, promise to improve adversarial robustness. We categorize such adaptive test-time defenses, explain their potential benefits and draw…
HiP: Hierarchical Perceiver
Joao Carreira, Skanda Koppula, Daniel Zoran +10
General perception systems such as Perceivers can process arbitrary modalities in any combination and are able to handle up to a few hundred thousand inputs. They achieve this gene…