Learning and Mining with Noisy Labels
Abstract
Index Terms
- Learning and Mining with Noisy Labels
Recommendations
Transductive Multilabel Learning via Label Set Propagation
The problem of multilabel classification has attracted great interest in the last decade, where each instance can be assigned with a set of multiple class labels simultaneously. It has a wide variety of real-world applications, e.g., automatic image ...
Collaborative Learning with Pseudo Labels for Robust Classification in the Presence of Noisy Labels
Computer Vision – ECCV 2020 WorkshopsAbstractSupervised learning depends on labels of dataset to train models with desired properties. Therefore, data containing mislabeled samples (a.k.a. noisy labels) can deteriorate supervised learning performance significantly as it makes models to be ...
Zero-Shot Learning with Noisy Labels
AbstractZero-shot learning (ZSL) is an attractive technique that can recognize novel object classes without any visual examples, but most existing methods assume that the class labels of the training instances from seen classes are accurate and reliable. ...
Comments
Information & Contributors
Information
Published In

Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Tutorial
Funding Sources
- JST CREST Grant
- Institute for AI and Beyond UTokyo
- NSFC Young Scientists Fund
- RGC Early Career Scheme
- ARC DECRA Project
- JST AIP Acceleration Research Grant
- Guangdong Basic and Applied Basic Research Foundation
- NSF IIS Grant
Conference
Acceptance Rates
Upcoming Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 252Total Downloads
- Downloads (Last 12 months)51
- Downloads (Last 6 weeks)2
Other Metrics
Citations
Cited By
View allView Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in