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Anne: Adaptive Nearest Neighbors and Eigenvector-Based Sample Selection for Robust Learning with Noisy Labels

https://doi.org/10.2139/ssrn.4683589
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Practical Computer Vision Applications Using Deep Learning with CNNs
inferredcites →https://doi.org/10.1007/978-1-4842-6168-2_4openalex-extracted
VGG Network
inferredcites →https://doi.org/10.1007/978-3-031-19806-9_2openalex-extracted
Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels
inferredcites →https://doi.org/10.1007/978-3-319-46493-0_38openalex-extracted
Identity Mappings in Deep Residual Networks
inferredcites →https://doi.org/10.1016/j.mlwa.2021.100134openalex-extracted
Deep learning in computer vision: A critical review of emerging techniques and application scenarios
inferredcites →https://doi.org/10.1016/j.patcog.2022.109013openalex-extracted
LongReMix: Robust learning with high confidence samples in a noisy label environment
inferredcites →https://doi.org/10.1038/s41746-020-00376-2openalex-extracted
Deep learning-enabled medical computer vision
inferredcites →https://doi.org/10.1109/cvpr.2017.240openalex-extracted
Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach
inferredcites →https://doi.org/10.1109/cvpr.2018.00906openalex-extracted
Iterative Learning with Open-set Noisy Labels
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01374openalex-extracted
Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00654openalex-extracted
Multi-Objective Interpolation Training for Robustness to Label Noise
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00998openalex-extracted
A Second-Order Approach to Learning with Instance-Dependent Label Noise
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01613openalex-extracted
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation
inferredcites →https://doi.org/10.1109/cvprw.2009.5206848openalex-extracted
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/iccv.2019.00019openalex-extracted
NLNL: Negative Learning for Noisy Labels
inferredcites →https://doi.org/10.1109/iccv.2019.00342openalex-extracted
O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks
inferredcites →https://doi.org/10.1109/iccv48922.2021.00935openalex-extracted
Learning from Noisy Data with Robust Representation Learning
inferredcites →https://doi.org/10.1109/tnet.2017.2680245openalex-extracted
An Online Learning Approach to Improving the Quality of Crowd-Sourcing
inferredcites →https://doi.org/10.1109/tnnls.2013.2292894openalex-extracted
Classification in the Presence of Label Noise: A Survey
inferredcites →https://doi.org/10.1109/tnnls.2022.3152527openalex-extracted
Learning From Noisy Labels With Deep Neural Networks: A Survey
inferredcites →https://doi.org/10.1109/tpami.2015.2456899openalex-extracted
Classification with Noisy Labels by Importance Reweighting
inferredcites →https://doi.org/10.1109/wacv51458.2022.00046openalex-extracted
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels
inferredcites →https://doi.org/10.1109/wacv56688.2023.00232openalex-extracted
Instance-Dependent Noisy Label Learning via Graphical Modelling
inferredcites →https://doi.org/10.1109/wacv56688.2023.00531openalex-extracted
Bootstrapping the Relationship Between Images and Their Clean and Noisy Labels
inferredcites →https://doi.org/10.1145/2745844.2745874openalex-extracted
An Online Learning Approach to Improving the Quality of Crowd-Sourcing
inferredcites →https://doi.org/10.1145/3502287openalex-extracted
A Systematic Review on Data Scarcity Problem in Deep Learning: Solution and Applications
inferredcites →https://doi.org/10.1609/aaai.v36i6.20654openalex-extracted
Uncertainty-Aware Learning against Label Noise on Imbalanced Datasets
inferredcites →https://doi.org/10.48550/arxiv.1706.02613openalex-extracted
Decoupling "when to update" from "how to update"
inferredcites →https://doi.org/10.48550/arxiv.1708.02862openalex-extracted
WebVision Database: Visual Learning and Understanding from Web Data
inferredcites →https://doi.org/10.48550/arxiv.1710.09412openalex-extracted
mixup: Beyond Empirical Risk Minimization
inferredcites →https://doi.org/10.48550/arxiv.1804.06872openalex-extracted
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.1805.07836openalex-extracted
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.1806.02612openalex-extracted
Dimensionality-Driven Learning with Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.1810.11874openalex-extracted
Learning with Bad Training Data via Iterative Trimmed Loss Minimization
inferredcites →https://doi.org/10.48550/arxiv.1901.11300openalex-extracted
Robust Inference via Generative Classifiers for Handling Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.1906.00189openalex-extracted
Are Anchor Points Really Indispensable in Label-Noise Learning?
inferredcites →https://doi.org/10.48550/arxiv.2003.02752openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2006.07836openalex-extracted
Part-dependent Label Noise: Towards Instance-dependent Label Noise
inferredcites →https://doi.org/10.48550/arxiv.2007.00151openalex-extracted
Early-Learning Regularization Prevents Memorization of Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.2012.11854openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2102.05291openalex-extracted
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.2102.11628openalex-extracted
FINE Samples for Learning with Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.2106.15853openalex-extracted
Understanding and Improving Early Stopping for Learning with Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.2108.11035openalex-extracted
NGC: A Unified Framework for Learning with Open-World Noisy Data
inferredcites →https://doi.org/10.48550/arxiv.2109.02986openalex-extracted
Instance-dependent Label-noise Learning under a Structural Causal Model
inferredcites →https://doi.org/10.48550/arxiv.2111.11288openalex-extracted
SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise
inferredcites →https://doi.org/10.48550/arxiv.2202.14026openalex-extracted
Robust Training under Label Noise by Over-parameterization
inferredcites →https://doi.org/10.48550/arxiv.2206.02791openalex-extracted
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation
inferredcites →https://doi.org/10.48550/arxiv.2207.14476openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2209.00906openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2210.08826openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2211.11355openalex-extracted
Blind Knowledge Distillation for Robust Image Classification
inferredcites →https://doi.org/10.5244/c.35.264openalex-extracted
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels
inferredcites →https://doi.org/10.5555/3327757.3327944openalex-extracted
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
inferredcites →https://doi.org/10.57702/zp44cu3gopenalex-extracted
Learning Multiple Layers of Features from Tiny Images
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