Learning from Noisy Labels with Deep Neural Networks (original) (raw)
Related papers
DeepCleanNet: Training Deep Convolutional Neural Network with Extremely Noisy Labels
2020
Learning From Noisy Labels With Deep Neural Networks: A Survey
IEEE Transactions on Neural Networks and Learning Systems, 2022
Image classification with deep learning in the presence of noisy labels: A survey
Knowledge-Based Systems, 2021
Learning from Noisy Labels with Noise Modeling Network
ArXiv, 2020
Making Deep Neural Networks Robust to Label Noise: Cross-Training With a Novel Loss Function
IEEE Access, 2019
Deep Learning Classification with Noisy Labels
2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2020
DCBT-Net: Training Deep Convolutional Neural Networks With Extremely Noisy Labels
IEEE Access
Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
Augmentation Strategies for Learning with Noisy Labels
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks
2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019
Label Noise Types and Their Effects on Deep Learning
arXiv (Cornell University), 2020
A Good Representation Detects Noisy Labels
ArXiv, 2021
Which Strategies Matter for Noisy Label Classification? Insight into Loss and Uncertainty
ArXiv, 2020
Synergistic Network Learning and Label Correction for Noise-Robust Image Classification
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022
MetaLabelNet: Learning to Generate Soft-Labels From Noisy-Labels
IEEE Transactions on Image Processing
An Effective Label Noise Model for
Proceedings of the 2019 Conference of the North
Making Neural Networks Robust to Label Noise: a Loss Correction Approach
ArXiv, 2016
IRJET, 2020
Learning to Combat Noisy Labels via Classification Margins
arXiv (Cornell University), 2021
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model
2021
Robustness and reliability when training with noisy labels
ArXiv, 2021
Learning to Bootstrap for Combating Label Noise
arXiv (Cornell University), 2022
Learning deep visual object models from noisy web data: How to make it work
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017
Data Expansion Approach with Attention Mechanism for Learning with Noisy Labels
International Journal on Artificial Intelligence Tools
Error-Bounded Correction of Noisy Labels
2020
Deep Label Distribution Learning With Label Ambiguity
IEEE Transactions on Image Processing
Noisy Character Recognition Using Deep Convolutional Neural Networks
2017
Generation and Analysis of Feature-Dependent Pseudo Noise for Training Deep Neural Networks
2021
Fidelity Estimation Improves Noisy-Image Classification With Pretrained Networks
IEEE Signal Processing Letters, 2021
Noisy training for deep neural networks in speech recognition
EURASIP Journal on Audio, Speech, and Music Processing, 2015
Noisy image enhancements using deep learning techniques
International Journal of Electrical and Computer Engineering (IJECE)
International Journal of Electrical and Computer Engineering (IJECE), 2024
Consistency Regularization on Clean Samples for Learning with Noisy Labels
IEICE Transactions on Information and Systems, 2022
Instance-Dependent Noisy Label Learning via Graphical Modelling
Cornell University - arXiv, 2022
Learning From Noisy Singly-labeled Data
ArXiv, 2018