conftrace_

label noise

498 papers

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Co-occurring keywords

noisy label (321) noisy label learning (163) deep neural network (1803) semi-supervised learning (2341) weakly supervised learning (1328) text classification (6793) image classification (1944) data augmentation (3052) representation learning (6206) active learning (1301)

Papers

Robustness of conditional GANs to noisy labels NIPS 2018
Collaborative Learning for Deep Neural Networks NIPS 2018
Co-teaching: Robust training of deep neural networks with extremely noisy labels NIPS 2018
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels NIPS 2018
Masking: A New Perspective of Noisy Supervision NIPS 2018
Improving DNNs Trained with Non-Native Transcriptions Using Knowledge Distillation and Target Interpolation INTERSPEECH 2018
A Theory of Learning with Corrupted Labels JMLR 2018
Cost-Sensitive Learning with Noisy Labels JMLR 2018
Learning to Reweight Examples for Robust Deep Learning ICML 2018
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels ICML 2018
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
Decoupling "when to update" from "how to update" NIPS 2017
Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks NIPS 2017
Efficient PAC Learning from the Crowd COLT 2017
Finding Patterns in Noisy Crowds: Regression-based Annotation Aggregation for Crowdsourced Data EMNLP 2017
Attend in Groups: A Weakly-Supervised Deep Learning Framework for Learning From Web Data CVPR 2017
Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing NIPS 2016
Data Programming: Creating Large Training Sets, Quickly NIPS 2016
Training Deep Neural Networks via Direct Loss Minimization ICML 2016
Noisy Label Recovery for Shadow Detection in Unfamiliar Domains CVPR 2016
DisturbLabel: Regularizing CNN on the Loss Layer CVPR 2016
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning NIPS 2016
Loss factorization, weakly supervised learning and label noise robustness ICML 2016