Towards Understanding the Data Dependency of Mixup-style Training

14 October 2021

Papers citing "Towards Understanding the Data Dependency of Mixup-style Training"

9 / 9 papers shown

Title
SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe Yuxin Xiao Shujian Zhang Wenxuan Zhou Marzyeh Ghassemi Sanqiang Zhao 58 0 0 07 Oct 2024
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation Muthuraman Chidambaram Rong Ge AAML 18 0 0 10 Feb 2024
Pushing Boundaries: Mixup's Influence on Neural Collapse Quinn Fisher Haoming Meng V. Papyan AAML UQCV 36 5 0 09 Feb 2024
Tailoring Mixup to Data for Calibration Quentin Bouniot Pavlo Mozharovskyi Florence dÁlché-Buc 53 1 0 02 Nov 2023
On the Limitations of Temperature Scaling for Distributions with Overlaps Muthuraman Chidambaram Rong Ge UQCV 24 4 0 01 Jun 2023
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective Chanwoo Park Sangdoo Yun Sanghyuk Chun AAML 13 32 0 21 Aug 2022
Universum-inspired Supervised Contrastive Learning Aiyang Han Chuanxing Geng Songcan Chen SSL 21 3 0 22 Apr 2022
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity Jang-Hyun Kim Wonho Choo Hosan Jeong Hyun Oh Song 195 176 0 05 Feb 2021
Bag of Tricks for Image Classification with Convolutional Neural Networks Tong He Zhi-Li Zhang Hang Zhang Zhongyue Zhang Junyuan Xie Mu Li 216 1,398 0 04 Dec 2018