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Uncertainty-Aware Cross Entropy for Robust Learning with Noisy Labels

https://doi.org/10.2139/ssrn.5139331
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ImageNet Large Scale Visual Recognition Challenge
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Multiple object tracking: A literature review
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Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey
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SigCo: Eliminate the inter-class competition via sigmoid for learning with noisy labels
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Meta-learning-based sample discrimination framework for improving dynamic selection of classifiers under label noise
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ECLB: Efficient contrastive learning on bi-level for noisy labels
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Learning with noisy labels for robust fatigue detection
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Image classification based on enhanced learning of global and local features with structural priors
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Gradient-based learning applied to document recognition
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ImageNet: A large-scale hierarchical image database
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Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
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.2018.00582openalex-snapshot
Joint Optimization Framework for Learning with Noisy Labels
inferredcites →https://doi.org/10.1109/cvpr.2019.00718openalex-snapshot
Probabilistic End-To-End Noise Correction for Learning With Noisy Labels
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01374openalex-snapshot
Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00515openalex-snapshot
Jo-SRC: A Contrastive Approach for Combating Noisy Labels
inferredcites →https://doi.org/10.1109/iccv.2019.00041openalex-snapshot
Symmetric Cross Entropy for Robust Learning With Noisy Labels
inferredcites →https://doi.org/10.1109/iccv.2019.00524openalex-snapshot
Deep Self-Learning From Noisy Labels
inferredcites →https://doi.org/10.1109/iccv48922.2021.00014openalex-snapshot
Learning with Noisy Labels via Sparse Regularization
inferredcites →https://doi.org/10.1109/iccv51070.2023.00176openalex-snapshot
Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples
inferredcites →https://doi.org/10.1109/jiot.2024.3462544openalex-snapshot
Meta-Learning Guided Label Noise Distillation for Robust Signal Modulation Classification
inferredcites →https://doi.org/10.1109/taffc.2017.2740923openalex-snapshot
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
inferredcites →https://doi.org/10.1109/tcsvt.2024.3400876openalex-snapshot
Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier Is All You Need
inferredcites →https://doi.org/10.1109/tnnls.2017.2699783openalex-snapshot
Multiclass Learning With Partially Corrupted Labels
inferredcites →https://doi.org/10.1109/tpami.2015.2456899openalex-snapshot
Classification with Noisy Labels by Importance Reweighting
inferredcites →https://doi.org/10.1109/tpami.2017.2699184openalex-snapshot
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
inferredcites →https://doi.org/10.1109/tpami.2022.3212594openalex-snapshot
Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook
inferredcites →https://doi.org/10.1109/tpami.2023.3236459openalex-snapshot
Asymmetric Loss Functions for Noise-Tolerant Learning: Theory and Applications
inferredcites →https://doi.org/10.1109/tpami.2024.3434373openalex-snapshot
Transformer-Based Visual Segmentation: A Survey
inferredcites →https://doi.org/10.1109/tsmcb.2012.2223460openalex-snapshot
Noise Tolerance Under Risk Minimization
inferredcites →https://doi.org/10.1111/exsy.13424openalex-snapshot
Efficient adaptive ensembling for image classification
inferredcites →https://doi.org/10.1609/aaai.v31i1.10894openalex-snapshot
Robust Loss Functions under Label Noise for Deep Neural Networks
inferredcites →https://doi.org/10.1609/aaai.v35i13.17363openalex-snapshot
Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise
inferredcites →https://doi.org/10.1609/aaai.v38i5.28284openalex-snapshot
Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning
inferredcites →https://doi.org/10.2139/ssrn.4470983openalex-snapshot
Meta-Learning Guided Label Noise Distillation for Robust Signal Modulation Classification
inferredcites →https://doi.org/10.24963/ijcai.2020/305openalex-snapshot
Can Cross Entropy Loss Be Robust to Label Noise?
inferredcites →https://doi.org/10.3929/ethz-b-000608965openalex-snapshot
Visual Object Tracking With Discriminative Filters and Siamese Networks: A Survey and Outlook
inferredcites →https://doi.org/10.48550/arxiv.1706.02613openalex-snapshot
Decoupling "when to update" from "how to update"
inferredcites →https://doi.org/10.48550/arxiv.1708.02862openalex-snapshot
WebVision Database: Visual Learning and Understanding from Web Data
inferredcites →https://doi.org/10.48550/arxiv.1712.05055openalex-snapshot
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
inferredcites →https://doi.org/10.48550/arxiv.1803.09050openalex-snapshot
Learning to Reweight Examples for Robust Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.1805.07836openalex-snapshot
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.1901.04215openalex-snapshot
How does Disagreement Help Generalization against Label Corruption?
inferredcites →https://doi.org/10.48550/arxiv.2006.07805openalex-snapshot
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning
inferredcites →https://doi.org/10.48550/arxiv.2006.13554openalex-snapshot
Normalized Loss Functions for Deep Learning with Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.2007.00151openalex-snapshot
Early-Learning Regularization Prevents Memorization of Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.2010.02347openalex-snapshot
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
inferredcites →https://doi.org/10.48550/arxiv.2011.10077openalex-snapshot
Error-Bounded Correction of Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.2103.07756openalex-snapshot
Learning with Feature-Dependent Label Noise: A Progressive Approach
inferredcites →https://doi.org/10.48550/arxiv.2106.03110openalex-snapshot
Asymmetric Loss Functions for Learning with Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.2110.12088openalex-snapshot
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
inferredcites →https://doi.org/10.48550/arxiv.2205.00186openalex-snapshot
Reliable Label Correction is a Good Booster When Learning with Extremely Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.2412.02373openalex-snapshot
Active Negative Loss: A Robust Framework for Learning with Noisy Labels
inferredcites →https://doi.org/10.5555/3327757.3327944openalex-snapshot
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
inferredcites →https://doi.org/10.57702/zp44cu3gopenalex-snapshot
Learning Multiple Layers of Features from Tiny Images
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