When does Privileged Information Explain Away Label Noise?

1 code implementation • 3 Mar 2023 • Guillermo Ortiz-Jimenez, Mark Collier, Anant Nawalgaria, Alexander D'Amour, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou

Leveraging privileged information (PI), or features available during training but not at test time, has recently been shown to be an effective method for addressing label noise.

Massively Scaling Heteroscedastic Classifiers

no code implementations • 30 Jan 2023 • Mark Collier, Rodolphe Jenatton, Basil Mustafa, Neil Houlsby, Jesse Berent, Effrosyni Kokiopoulou

Heteroscedastic classifiers, which learn a multivariate Gaussian distribution over prediction logits, have been shown to perform well on image classification problems with hundreds to thousands of classes.

Classification Contrastive Learning +1

Deep Classifiers with Label Noise Modeling and Distance Awareness

no code implementations • 6 Oct 2021 • Vincent Fortuin, Mark Collier, Florian Wenzel, James Allingham, Jeremiah Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou

Uncertainty estimation in deep learning has recently emerged as a crucial area of interest to advance reliability and robustness in safety-critical applications.

Out-of-Distribution Detection