80 citations · 115 across the 6 of their papers we have counts for
11 papers
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…
On-target Adaptation
Dequan Wang, Shaoteng Liu, Sayna Ebrahimi +2
Domain adaptation seeks to mitigate the shift between training on the \emph{source} domain and testing on the \emph{target} domain. Most adaptation methods rely on the source data…
Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks
Dequan Wang, An Ju, Evan Shelhamer +2
Adversarial attacks optimize against models to defeat defenses. Existing defenses are static, and stay the same once trained, even while attacks change. We argue that models should…
It Is Likely That Your Loss Should be a Likelihood
Mark Hamilton, Evan Shelhamer, William T. Freeman
Many common loss functions such as mean-squared-error, cross-entropy, and reconstruction loss are unnecessarily rigid. Under a probabilistic interpretation, these common losses cor…
Tent: Fully Test-time Adaptation by Entropy Minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu +2
A model must adapt itself to generalize to new and different data during testing. In this setting of fully test-time adaptation the model has only the test data and its own paramet…
Dynamic Scale Inference by Entropy Minimization
Dequan Wang, Evan Shelhamer, Bruno Olshausen +1
Given the variety of the visual world there is not one true scale for recognition: objects may appear at drastically different sizes across the visual field. Rather than enumerate…