CNLL: A Semi-supervised Approach For Continual Noisy Label Learning

Author:

Karim Nazmul1,Khalid Umar1,Esmaeili Ashkan1,Rahnavard Nazanin1

Affiliation:

1. University of Central Florida,Department of Electrical and Computer Engineering,USA

Funder

National Science Foundation

Publisher

IEEE

Reference83 articles.

1. Lifelong learning with dynamically expandable networks;yoon,2017

2. Probabilistic End-To-End Noise Correction for Learning With Noisy Labels

3. Jo-SRC: A Contrastive Approach for Combating Noisy Labels

4. Learning from massive noisy labeled data for image classification;xiao;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2015

5. Detect-and-describe: Joint learning framework for detection and description of objects;MATEC Web of Conferences,2019

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