Out-of-Domain Generalization from a Single Source: An Uncertainty Quantification Approach

5 August 2021

Papers citing "Out-of-Domain Generalization from a Single Source: An Uncertainty Quantification Approach"

8 / 8 papers shown

Title
Producing Plankton Classifiers that are Robust to Dataset Shift Cheng Chen S. Kyathanahally Marta Reyes Stefanie Merkli E. Merz Emanuele Francazi Marvin Hoege F. Pomati M. Baity-Jesi 14 2 0 25 Jan 2024
Are Data-driven Explanations Robust against Out-of-distribution Data? Tang Li Fengchun Qiao Mengmeng Ma Xiangkai Peng OODD OOD 20 10 0 29 Mar 2023
Generalizing to Unseen Domains: A Survey on Domain Generalization Jindong Wang Cuiling Lan Chang-Shu Liu Yidong Ouyang Tao Qin Wang Lu Yiqiang Chen Wenjun Zeng Philip S. Yu OOD 43 1,165 0 02 Mar 2021
Disentangling Adversarial Robustness and Generalization David Stutz Matthias Hein Bernt Schiele AAML OOD 178 271 0 03 Dec 2018
Bayesian Model-Agnostic Meta-Learning Taesup Kim Jaesik Yoon Ousmane Amadou Dia Sungwoong Kim Yoshua Bengio Sungjin Ahn UQCV BDL 188 495 0 11 Jun 2018
Probabilistic Model-Agnostic Meta-Learning Chelsea Finn Kelvin Xu Sergey Levine BDL 165 664 0 07 Jun 2018
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 240 11,568 0 09 Mar 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,042 0 06 Jun 2015