When and How Mixup Improves Calibration

11 February 2021

Papers citing "When and How Mixup Improves Calibration"

10 / 10 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 59 1 0 29 Jan 2025
Revisiting Confidence Estimation: Towards Reliable Failure Prediction Fei Zhu Xu-Yao Zhang Zhen Cheng Cheng-Lin Liu UQCV 39 10 0 05 Mar 2024
Towards Calibrated Deep Clustering Network Yuheng Jia Jianhong Cheng Hui Liu Junhui Hou UQCV 38 1 0 04 Mar 2024
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation Muthuraman Chidambaram Rong Ge AAML 13 0 0 10 Feb 2024
Tailoring Mixup to Data for Calibration Quentin Bouniot Pavlo Mozharovskyi Florence dÁlché-Buc 53 1 0 02 Nov 2023
Towards Generalizable Deepfake Detection by Primary Region Regularization Harry Cheng Yangyang Guo Tianyi Wang Liqiang Nie Mohan S. Kankanhalli 19 0 0 24 Jul 2023
On the Limitations of Temperature Scaling for Distributions with Overlaps Muthuraman Chidambaram Rong Ge UQCV 19 3 0 01 Jun 2023
Annealing Double-Head: An Architecture for Online Calibration of Deep Neural Networks Erdong Guo D. Draper Maria de Iorio 19 0 0 27 Dec 2022
An Unconstrained Layer-Peeled Perspective on Neural Collapse Wenlong Ji Yiping Lu Yiliang Zhang Zhun Deng Weijie J. Su 122 83 0 06 Oct 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,635 0 05 Dec 2016