label noise

Spotting Spurious Data with Neural Networks NAACL 2018 Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise NeurIPS 2018 Visual Supervision in Bootstrapped Information Extraction EMNLP 2018 Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining ACL 2018 Attend in Groups: A Weakly-Supervised Deep Learning Framework for Learning From Web Data CVPR 2017 Decoupling "when to update" from "how to update" NeurIPS 2017 Efficient PAC Learning from the Crowd COLT 2017 Finding Patterns in Noisy Crowds: Regression-based Annotation Aggregation for Crowdsourced Data EMNLP 2017 Improving Learning-from-Crowds through Expert Validation IJCAI 2017 Learning From Noisy Large-Scale Datasets With Minimal Supervision CVPR 2017 Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach CVPR 2017 Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks NeurIPS 2017 Data Programming: Creating Large Training Sets, Quickly NeurIPS 2016 DisturbLabel: Regularizing CNN on the Loss Layer CVPR 2016 Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing NeurIPS 2016 Loss factorization, weakly supervised learning and label noise robustness ICML 2016 Meta–Gradient Boosted Decision Tree Model for Weight and Target Learning ICML 2016 More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning NeurIPS 2016 Noisy Label Recovery for Shadow Detection in Unfamiliar Domains CVPR 2016 Training Deep Neural Networks via Direct Loss Minimization ICML 2016 A Structural Smoothing Framework For Robust Graph Comparison NeurIPS 2015 Efficient Label Collection for Unlabeled Image Datasets CVPR 2015 Learning from Corrupted Binary Labels via Class-Probability Estimation ICML 2015 Learning from Data with Heterogeneous Noise using SGD AISTATS 2015 Learning From Massive Noisy Labeled Data for Image Classification CVPR 2015