743 citations · 2.1k across the 27 of their papers we have counts for
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Simplicity Bias Leads to Amplified Performance Disparities
Samuel J. Bell, Levent Sagun
Which parts of a dataset will a given model find difficult? Recent work has shown that SGD-trained models have a bias towards simplicity, leading them to prioritize learning a majo…
Measuring and signing fairness as performance under multiple stakeholder distributions
David Lopez-Paz, Diane Bouchacourt, Levent Sagun +1
As learning machines increase their influence on decisions concerning human lives, analyzing their fairness properties becomes a subject of central importance. Yet, our best tools…
Understanding out-of-distribution accuracies through quantifying difficulty of test samples
Berfin Simsek, Melissa Hall, Levent Sagun
Existing works show that although modern neural networks achieve remarkable generalization performance on the in-distribution (ID) dataset, the accuracy drops significantly on the…
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
Priya Goyal, Quentin Duval, Isaac Seessel +5
Discriminative self-supervised learning allows training models on any random group of internet images, and possibly recover salient information that helps differentiate between the…
Fairness Indicators for Systematic Assessments of Visual Feature Extractors
Priya Goyal, Adriana Romero Soriano, Caner Hazirbas +2
Does everyone equally benefit from computer vision systems? Answers to this question become more and more important as computer vision systems are deployed at large scale, and can…