Confidence Calibration for Convolutional Neural Networks Using Structured Dropout

23 June 2019

Papers citing "Confidence Calibration for Convolutional Neural Networks Using Structured Dropout"

7 / 7 papers shown

Title
Hardware-Aware Neural Dropout Search for Reliable Uncertainty Prediction on FPGA Zehuan Zhang Hongxiang Fan Hao Chen Lukasz Dudziak Wayne Luk BDL 40 0 0 23 Jun 2024
Rethinking Soft Label in Label Distribution Learning Perspective Seungbum Hong Jihun Yoon Bogyu Park Min-Kook Choi 31 0 0 31 Jan 2023
The Devil is in the Margin: Margin-based Label Smoothing for Network Calibration Bingyuan Liu Ismail Ben Ayed Adrian Galdran Jose Dolz UQCV 24 65 0 30 Nov 2021
A Survey of Uncertainty in Deep Neural Networks J. Gawlikowski Cedrique Rovile Njieutcheu Tassi Mohsin Ali Jongseo Lee Matthias Humt ... R. Roscher Muhammad Shahzad Wen Yang R. Bamler Xiaoxiang Zhu BDL UQCV OOD 32 1,109 0 07 Jul 2021
Learning to Cascade: Confidence Calibration for Improving the Accuracy and Computational Cost of Cascade Inference Systems Shohei Enomoto Takeharu Eda UQCV 43 17 0 15 Apr 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,660 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015