4.3k citations · 10.2k across the 23 of their papers we have counts for
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Seasoning Model Soups for Robustness to Adversarial and Natural Distribution Shifts
Francesco Croce, Sylvestre-Alvise Rebuffi, Evan Shelhamer +1
Adversarial training is widely used to make classifiers robust to a specific threat or adversary, such as -norm bounded perturbations of a given -norm. However, existing…
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…
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…
Perceiver IO: A General Architecture for Structured Inputs & Outputs
Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac +12
A central goal of machine learning is the development of systems that can solve many problems in as many data domains as possible. Current architectures, however, cannot be applied…
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…
Infinite Mixture Prototypes for Few-Shot Learning
Kelsey R. Allen, Evan Shelhamer, Hanul Shin +1
We propose infinite mixture prototypes to adaptively represent both simple and complex data distributions for few-shot learning. Our infinite mixture prototypes represent each clas…
Zero-Shot Visual Imitation
Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo +7
The current dominant paradigm for imitation learning relies on strong supervision of expert actions to learn both 'what' and 'how' to imitate. We pursue an alternative paradigm whe…
Loss is its own Reward: Self-Supervision for Reinforcement Learning
Evan Shelhamer, Parsa Mahmoudieh, Max Argus +1
Reinforcement learning optimizes policies for expected cumulative reward. Need the supervision be so narrow? Reward is delayed and sparse for many tasks, making it a difficult and…