k-Mixup Regularization for Deep Learning via Optimal Transport

5 June 2021

Papers citing "k-Mixup Regularization for Deep Learning via Optimal Transport"

7 / 7 papers shown

Title
Tailoring Mixup to Data for Calibration Quentin Bouniot Pavlo Mozharovskyi Florence dÁlché-Buc 61 1 0 02 Nov 2023
A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability Chengtai Cao Fan Zhou Yurou Dai Jianping Wang Kunpeng Zhang AAML 24 28 0 21 Dec 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
GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction Jiacheng Zhu Jielin Qiu Zhuolin Yang Douglas Weber M. Rosenberg Emerson Liu Bo-wen Li Ding Zhao OOD 28 13 0 02 Aug 2022
Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization Xiaojun Xu Jacky Y. Zhang Evelyn Ma Danny Son Oluwasanmi Koyejo Bo-wen Li 20 10 0 03 Feb 2022
Preventing Manifold Intrusion with Locality: Local Mixup Raphael Baena Lucas Drumetz Vincent Gripon AAML 21 15 0 12 Jan 2022
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity Jang-Hyun Kim Wonho Choo Hosan Jeong Hyun Oh Song 197 176 0 05 Feb 2021