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Augmenting Monte Carlo Dropout Classification Models with Unsupervised
  Learning Tasks for Detecting and Diagnosing Out-of-Distribution Faults

Augmenting Monte Carlo Dropout Classification Models with Unsupervised Learning Tasks for Detecting and Diagnosing Out-of-Distribution Faults

10 September 2019
Baihong Jin
Yingshui Tan
Yuxin Chen
Alberto L. Sangiovanni-Vincentelli
ArXiv (abs)PDFHTML

Papers citing "Augmenting Monte Carlo Dropout Classification Models with Unsupervised Learning Tasks for Detecting and Diagnosing Out-of-Distribution Faults"

2 / 2 papers shown
Title
Dealing with Distribution Mismatch in Semi-supervised Deep Learning for
  Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature
  Densities
Dealing with Distribution Mismatch in Semi-supervised Deep Learning for Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature Densities
Saul Calderon-Ramirez
Shengxiang-Yang
David Elizondo
Armaghan Moemeni
OOD
56
24
0
17 Aug 2021
Exploiting Uncertainties from Ensemble Learners to Improve
  Decision-Making in Healthcare AI
Exploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AI
Yingshui Tan
Baihong Jin
Xiangyu Yue
Yuxin Chen
Alberto L. Sangiovanni-Vincentelli
59
7
0
12 Jul 2020
1