RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks-Reference-Cited by-同舟云学术

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RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks

Published:2024-08-24 Issue: Volume:34 Page:1155-1165
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Hwang Seong-Hyeon¹^ORCID,Kim Minsu¹^ORCID,Whang Steven Euijong¹^ORCID

Affiliation:

1. KAIST, Daejeon, Republic of Korea

Funder

National Research Foundation of Korea

Samsung Electronics Co., Ltd.

Institute of Information & Communications Technology Planning & Evaluation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671993

Reference54 articles.

1. Magne Aldrin. 2004. Carnegie Mellon University StatLib Dataset. http://lib.stat.cmu.edu/datasets/. Accessed: 2022-08--15.

2. Eric Arazo Diego Ortego Paul Albert Noel O'Connor and Kevin McGuinness. 2019. Unsupervised label noise modeling and loss correction. In ICML. 312--321.

3. Alan Joseph Bekker and Jacob Goldberger. 2016. Training deep neural-networks based on unreliable labels. In IEEE ICASSP. 2682--2686.

4. Haw-Shiuan Chang, Erik Learned-Miller, and Andrew McCallum. 2017. Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples. In NeurIPS.

5. Olivier Chapelle Jason Weston Léon Bottou and Vladimir Vapnik. 2000. Vicinal Risk Minimization. In NIPS. 416--422.

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