CRSSC: Salvage Reusable Samples from Noisy Data for Robust Learning-Reference-Cited by-同舟云学术

CRSSC: Salvage Reusable Samples from Noisy Data for Robust Learning

Published:2020-10-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th ACM International Conference on Multimedia
language:
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Author:

Sun Zeren¹,Hua Xian-Sheng²,Yao Yazhou¹,Wei Xiu-Shen¹,Hu Guosheng³,Zhang Jian⁴

Affiliation:

1. Nanjing University of Science and Technology, Nanjing, China

2. Alibaba Group, Hangzhou, China

3. AnyVision, Belfast, United Kingdom

4. University of Technology Sydney, Sydney, Australia

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3394171.3413978

Reference64 articles.

1. Haw-Shiuan Chang Erik Learned-Miller and Andrew McCallum. 2017a. Active bias: Training more accurate neural networks by emphasizing high variance samples. In Advances in Neural Information Processing Systems. 1002--1012. Haw-Shiuan Chang Erik Learned-Miller and Andrew McCallum. 2017a. Active bias: Training more accurate neural networks by emphasizing high variance samples. In Advances in Neural Information Processing Systems. 1002--1012.

2. Haw-Shiuan Chang Erik Learned-Miller and Andrew McCallum. 2017b. Active bias: Training more accurate neural networks by emphasizing high variance samples. In Advances in Neural Information Processing Systems. 1002--1012. Haw-Shiuan Chang Erik Learned-Miller and Andrew McCallum. 2017b. Active bias: Training more accurate neural networks by emphasizing high variance samples. In Advances in Neural Information Processing Systems. 1002--1012.

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