23 citations · 49 across the 9 of their papers we have counts for
Showing 2019Show all
2 papers · 1 filter
cs.LG2019★ 8 cited
On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration
Kanil Patel, William Beluch, Dan Zhang +2
Uncertainty estimates help to identify ambiguous, novel, or anomalous inputs, but the reliable quantification of uncertainty has proven to be challenging for modern deep networks.…
cs.CV2019★ 23 cited
Uncertainty Based Detection and Relabeling of Noisy Image Labels
Jan M. Köhler, Maximilian Autenrieth, William H. Beluch
Deep neural networks (DNNs) are powerful tools in computer vision tasks. However, in many realistic scenarios label noise is prevalent in the training images, and overfitting to th…