290 citations · 1.5k across the 25 of their papers we have counts for
4 papers · 1 filter
Scaling Out-of-Distribution Detection for Real-World Settings
Dan Hendrycks, Steven Basart, Mantas Mazeika +5
Detecting out-of-distribution examples is important for safety-critical machine learning applications such as detecting novel biological phenomena and self-driving cars. However, e…
DIODE: A Dense Indoor and Outdoor DEpth Dataset
Igor Vasiljevic, Nick Kolkin, Shanyi Zhang +8
We introduce DIODE, a dataset that contains thousands of diverse high resolution color images with accurate, dense, long-range depth measurements. DIODE (Dense Indoor/Outdoor DEpth…
Testing Robustness Against Unforeseen Adversaries
Max Kaufmann, Daniel Kang, Yi Sun +9
Adversarial robustness research primarily focuses on L_p perturbations, and most defenses are developed with identical training-time and test-time adversaries. However, in real-wor…
Natural Adversarial Examples
Dan Hendrycks, Kevin Zhao, Steven Basart +2
We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. The datasets are collected with a simple adversarial filtrati…