PseudoCal: A Source-Free Approach to Unsupervised Uncertainty
Calibration in Domain Adaptation

PseudoCal: A Source-Free Approach to Unsupervised Uncertainty Calibration in Domain Adaptation

14 July 2023

Jian Liang

Chuan-Sheng Foo

Papers citing "PseudoCal: A Source-Free Approach to Unsupervised Uncertainty Calibration in Domain Adaptation"

6 / 6 papers shown

Title
Unsupervised Domain Adaptation of Black-Box Source Models Haojian Zhang Yabin Zhang K. Jia Lei Zhang 122 51 0 08 Jan 2021
Improving model calibration with accuracy versus uncertainty optimization R. Krishnan Omesh Tickoo UQCV 183 157 0 14 Dec 2020
Improved Baselines with Momentum Contrastive Learning Xinlei Chen Haoqi Fan Ross B. Girshick Kaiming He SSL 240 3,367 0 09 Mar 2020
A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation Jian Liang Yunbo Wang Dapeng Hu R. He Jiashi Feng 161 105 0 05 Mar 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,660 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 252 9,134 0 06 Jun 2015