Co-occurring keywords · papers mentioning both
Papers
Improving generalization by controlling label-noise information in neural network weights
ICML 2020
Joint Entity and Relation Extraction with a Hybrid Transformer and Reinforcement Learning Based Model
AAAI 2020
Label Distribution for Learning with Noisy Labels
IJCAI 2020
Label-Noise Robust Domain Adaptation
ICML 2020
Learning From Noisy Anchors for One-Stage Object Detection
CVPR 2020
Learning in the Wild with Incremental Skeptical Gaussian Processes
IJCAI 2020
Learn with Noisy Data via Unsupervised Loss Correction for Weakly Supervised Reading Comprehension
COLING 2020
Model-agnostic Methods for Text Classification with Inherent Noise
COLING 2020
Normalized Loss Functions for Deep Learning with Noisy Labels
ICML 2020
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
ICML 2020
Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents
ACL 2020
Robust Federated Learning via Collaborative Machine Teaching
AAAI 2020
Robust Optimization for Fairness with Noisy Protected Groups
NeurIPS 2020
Robust training with ensemble consensus
ICLR 2020
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
ICML 2020
Task Agnostic Robust Learning on Corrupt Outputs by Correlation-Guided Mixture Density Networks
CVPR 2020
Training Noise-Robust Deep Neural Networks via Meta-Learning
CVPR 2020
Walking on Two Legs: Learning Image Segmentation with Noisy Labels
UAI 2020
Web-Supervised Network with Softly Update-Drop Training for Fine-Grained Visual Classification
AAAI 2020
An Effective Label Noise Model for DNN Text Classification
NAACL 2019
Are Anchor Points Really Indispensable in Label-Noise Learning?
NeurIPS 2019
A Topic-Aware Reinforced Model for Weakly Supervised Stance Detection
AAAI 2019
A Two-Stream Mutual Attention Network for Semi-Supervised Biomedical Segmentation with Noisy Labels
AAAI 2019
Combating Label Noise in Deep Learning using Abstention
ICML 2019
Co-Mining: Deep Face Recognition With Noisy Labels
ICCV 2019