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A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels

https://doi.org/10.1109/wacv.2018.00138

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/3-540-44533-1_22openalex-snapshot

Evaluating Noise Correction

inferredcites →https://doi.org/10.1007/978-3-031-01548-9openalex-snapshot

Introduction to Semi-Supervised Learning

inferredcites →https://doi.org/10.1007/978-3-319-10584-0_20openalex-snapshot

Simultaneous Detection and Segmentation

inferredcites →https://doi.org/10.1007/978-3-319-46478-7_5openalex-snapshot

Learning Visual Features from Large Weakly Supervised Data

inferredcites →https://doi.org/10.1007/978-3-319-46487-9_52openalex-snapshot

Webly-Supervised Video Recognition by Mutually Voting for Relevant Web Images and Web Video Frames

inferredcites →https://doi.org/10.1007/978-3-642-02319-4_50openalex-snapshot

Use of Classification Algorithms in Noise Detection and Elimination

inferredcites →https://doi.org/10.1007/978-3-642-04274-4_91openalex-snapshot

Learning SVMs from Sloppily Labeled Data

inferredcites →https://doi.org/10.1007/s11263-009-0275-4openalex-snapshot

The Pascal Visual Object Classes (VOC) Challenge

inferredcites →https://doi.org/10.1007/s11263-013-0620-5openalex-snapshot

Selective Search for Object Recognition

inferredcites →https://doi.org/10.1007/s11263-013-0636-xopenalex-snapshot

Image Classification with the Fisher Vector: Theory and Practice

inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot

ImageNet Large Scale Visual Recognition Challenge

inferredcites →https://doi.org/10.1016/j.neucom.2014.09.081openalex-snapshot

Making risk minimization tolerant to label noise

inferredcites →https://doi.org/10.1016/j.procs.2011.04.170openalex-snapshot

Classification of Seismic Windows Using Artificial Neural Networks

inferredcites →https://doi.org/10.1016/j.procs.2013.05.222openalex-snapshot

A Scalable Parallel LSQR Algorithm for Solving Large-scale Linear System for Tomographic Problems: A Case Study in Seismic Tomography

inferredcites →https://doi.org/10.1023/a:1022689900470openalex-snapshot

Instance-Based Learning Algorithms

inferredcites →https://doi.org/10.1023/a:1025832930864openalex-snapshot

Identifying and Handling Mislabelled Instances

inferredcites →https://doi.org/10.1093/oxfordjournals.aje.a117428openalex-snapshot

Bayesian Estimation of Disease Prevalence and the Parameters of Diagnostic Tests in the Absence of a Gold Standard

inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot

Gradient-based learning applied to document recognition

inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot

Going deeper with convolutions

inferredcites →https://doi.org/10.1109/cvpr.2015.7298658openalex-snapshot

Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection

inferredcites →https://doi.org/10.1109/cvpr.2015.7298885openalex-snapshot

Learning from massive noisy labeled data for image classification

inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot

Fully convolutional networks for semantic segmentation

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.240openalex-snapshot

Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach

inferredcites →https://doi.org/10.1109/cvpr.2017.311openalex-snapshot

Attend in Groups: A Weakly-Supervised Deep Learning Framework for Learning from Web Data

inferredcites →https://doi.org/10.1109/cvpr.2017.696openalex-snapshot

Learning from Noisy Large-Scale Datasets with Minimal Supervision

inferredcites →https://doi.org/10.1109/fgcn.2007.146openalex-snapshot

Identifying and Correcting Mislabeled Training Instances

inferredcites →https://doi.org/10.1109/iccv.2015.168openalex-snapshot

Webly Supervised Learning of Convolutional Networks

inferredcites →https://doi.org/10.1109/iccv.2017.211openalex-snapshot

Learning from Noisy Labels with Distillation

inferredcites →https://doi.org/10.1109/ijcnn.2010.5596659openalex-snapshot

Semi-supervised learning from imperfect data through particle cooperation and competition

inferredcites →https://doi.org/10.1109/tnnls.2013.2292894openalex-snapshot

Classification in the Presence of Label Noise: A Survey

inferredcites →https://doi.org/10.1109/tpami.2012.231openalex-snapshot

Learning Hierarchical Features for Scene Labeling

inferredcites →https://doi.org/10.1109/tpami.2015.2456899openalex-snapshot

Classification with Noisy Labels by Importance Reweighting

inferredcites →https://doi.org/10.1109/tpami.2018.2858821openalex-snapshot

Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning

inferredcites →https://doi.org/10.1109/tsmcb.2012.2223460openalex-snapshot

Noise Tolerance Under Risk Minimization

inferredcites →https://doi.org/10.1145/2016741.2016744openalex-snapshot

A model-driven partitioning and auto-tuning integrated framework for sparse matrix-vector multiplication on GPUs

inferredcites →https://doi.org/10.1145/3065386openalex-snapshot

ImageNet classification with deep convolutional neural networks

inferredcites →https://doi.org/10.1609/aaai.v31i1.11231openalex-snapshot

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

inferredcites →https://doi.org/10.1613/jair.606openalex-snapshot

Identifying Mislabeled Training Data

inferredcites →https://doi.org/10.2200/s00196ed1v01y200906aim006openalex-snapshot

Introduction to Semi-Supervised Learning

inferredcites →https://doi.org/10.48550/arxiv.1406.2080openalex-snapshot

Training Convolutional Networks with Noisy Labels

inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot

Very Deep Convolutional Networks for Large-Scale Image Recognition

inferredcites →https://doi.org/10.48550/arxiv.1412.6596openalex-snapshot

Training Deep Neural Networks on Noisy Labels with Bootstrapping

inferredcites →https://doi.org/10.48550/arxiv.1507.02672openalex-snapshot

Semi-Supervised Learning with Ladder Networks

inferredcites →https://doi.org/10.48550/arxiv.1603.02501openalex-snapshot

Mixture Proportion Estimation via Kernel Embedding of Distributions

inferredcites →https://doi.org/10.48550/arxiv.1606.01583openalex-snapshot

Semi-Supervised Learning with Generative Adversarial Networks

inferredcites →https://doi.org/10.48550/arxiv.1610.02242openalex-snapshot

Temporal Ensembling for Semi-Supervised Learning

inferredcites →https://doi.org/10.48550/arxiv.1705.10694openalex-snapshot

Deep Learning is Robust to Massive Label Noise

inferredcites →https://doi.org/10.48550/arxiv.1706.00038openalex-snapshot

Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks

inferredcites →https://doi.org/10.57702/zp44cu3gopenalex-snapshot

Learning Multiple Layers of Features from Tiny Images

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Collaborative Learning with Pseudo Labels for Robust Classification in the Presence of Noisy Labels

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Semi-supervised Learning from Active Noisy Soft Labels for Anomaly Detection

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Delving Deeper Into Clean Samples for Combating Noisy Labels

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Meta joint optimization: a holistic framework for noisy-labeled visual recognition

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BAW: learning from class imbalance and noisy labels with batch adaptation weighted loss

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SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels

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Data fusing and joint training for learning with noisy labels

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Anti-noise face: A resilient model for face recognition with labeled noise data

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CJC-net: A cyclical training method with joint loss and co-teaching strategy net for deep learning under noisy labels

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A two-stage denoising framework for zero-shot learning with noisy labels

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Image classification with deep learning in the presence of noisy labels: A survey

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Beyond clean data: Exploring the effects of label noise on object detection performance

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Semantic clustering based deduction learning for image recognition and classification

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Deep learning for mining protein data

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Using Under-Trained Deep Ensembles to Learn Under Extreme Label Noise: A Case Study for Sleep Apnea Detection

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ConfidentMix: Confidence-Guided Mixup for Learning With Noisy Labels

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Self-Learning With Rectification Strategy for Human Parsing

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Distilling Effective Supervision From Severe Label Noise

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Multi-Objective Interpolation Training for Robustness to Label Noise

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Joint Negative and Positive Learning for Noisy Labels

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Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling

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UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning

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SURE: SUrvey REcipes for Building Reliable and Robust Deep Networks

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