When and How Mixup Improves Calibration

When and How Mixup Improves Calibration

11 February 2021

Kenji Kawaguchi

Papers citing "When and How Mixup Improves Calibration"

17 / 17 papers shown

Title
Technical report on label-informed logit redistribution for better domain generalization in low-shot classification with foundation models Behraj Khan T. Syed 136 1 0 29 Jan 2025
Revisiting Confidence Estimation: Towards Reliable Failure Prediction Fei Zhu Xu-Yao Zhang Zhen Cheng Cheng-Lin Liu UQCV 49 10 0 05 Mar 2024
Towards Calibrated Deep Clustering Network Yuheng Jia Jianhong Cheng Hui Liu Junhui Hou UQCV 45 1 0 04 Mar 2024
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation Muthuraman Chidambaram Rong Ge AAML 18 0 0 10 Feb 2024
Tailoring Mixup to Data for Calibration Quentin Bouniot Pavlo Mozharovskyi Florence dÁlché-Buc 58 1 0 02 Nov 2023
Towards Generalizable Deepfake Detection by Primary Region Regularization Harry Cheng Yangyang Guo Tianyi Wang Liqiang Nie Mohan S. Kankanhalli 50 0 0 24 Jul 2023
On the Limitations of Temperature Scaling for Distributions with Overlaps Muthuraman Chidambaram Rong Ge UQCV 37 4 0 01 Jun 2023
Beyond Confidence: Reliable Models Should Also Consider Atypicality Mert Yuksekgonul Linjun Zhang James Y. Zou Carlos Guestrin 32 20 0 29 May 2023
Rethinking Confidence Calibration for Failure Prediction Fei Zhu Zhen Cheng Xu-Yao Zhang Cheng-Lin Liu UQCV 14 39 0 06 Mar 2023
Distilling Calibrated Student from an Uncalibrated Teacher Ishan Mishra Sethu Vamsi Krishna Deepak Mishra FedML 32 2 0 22 Feb 2023
Annealing Double-Head: An Architecture for Online Calibration of Deep Neural Networks Erdong Guo D. Draper Maria de Iorio 35 0 0 27 Dec 2022
LUMix: Improving Mixup by Better Modelling Label Uncertainty Shuyang Sun Jieneng Chen Ruifei He Alan Yuille Philip H. S. Torr Song Bai UQCV NoLa 15 5 0 29 Nov 2022
Reinforcement Learning with Stepwise Fairness Constraints Zhun Deng He Sun Zhiwei Steven Wu Linjun Zhang David C. Parkes FaML OffRL 37 11 0 08 Nov 2022
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective Chanwoo Park Sangdoo Yun Sanghyuk Chun AAML 21 32 0 21 Aug 2022
Towards Understanding the Data Dependency of Mixup-style Training Muthuraman Chidambaram Xiang Wang Yuzheng Hu Chenwei Wu Rong Ge UQCV 47 24 0 14 Oct 2021
An Unconstrained Layer-Peeled Perspective on Neural Collapse Wenlong Ji Yiping Lu Yiliang Zhang Zhun Deng Weijie J. Su 129 83 0 06 Oct 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