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Anti-Noise Face: A Resilient Model for Face Recognition with Labled Noise Data

https://doi.org/10.2139/ssrn.4718374
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inferredcites →https://doi.org/10.1007/978-3-030-01240-3_47openalex-extracted
The Devil of Face Recognition Is in the Noise
inferredcites →https://doi.org/10.1007/978-3-030-58621-8_43openalex-extracted
Sub-center ArcFace: Boosting Face Recognition by Large-Scale Noisy Web Faces
inferredcites →https://doi.org/10.1007/978-3-031-19778-9_6openalex-extracted
BoundaryFace: A Mining Framework with Noise Label Self-correction for Face Recognition
inferredcites →https://doi.org/10.1007/978-3-319-46487-9_6openalex-extracted
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
inferredcites →https://doi.org/10.1007/s10462-004-0751-8openalex-extracted
Class Noise vs. Attribute Noise: A Quantitative Study
inferredcites →https://doi.org/10.1109/cvpr.2005.202openalex-extracted
Learning a Similarity Metric Discriminatively, with Application to Face Verification
inferredcites →https://doi.org/10.1109/cvpr.2015.7298682openalex-extracted
FaceNet: A unified embedding for face recognition and clustering
inferredcites →https://doi.org/10.1109/cvpr.2016.434openalex-extracted
Deep Metric Learning via Lifted Structured Feature Embedding
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-extracted
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2018.00552openalex-extracted
CosFace: Large Margin Cosine Loss for Deep Face Recognition
inferredcites →https://doi.org/10.1109/cvpr.2018.00582openalex-extracted
Joint Optimization Framework for Learning with Noisy Labels
inferredcites →https://doi.org/10.1109/cvpr.2019.00482openalex-extracted
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
inferredcites →https://doi.org/10.1109/cvpr.2019.00800openalex-extracted
Unequal-Training for Deep Face Recognition With Long-Tailed Noisy Data
inferredcites →https://doi.org/10.1109/cvpr.2019.01216openalex-extracted
Noise-Tolerant Paradigm for Training Face Recognition CNNs
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00775openalex-extracted
Global-Local GCN: Large-Scale Label Noise Cleansing for Face Recognition
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01819openalex-extracted
AdaFace: Quality Adaptive Margin for Face Recognition
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/iccv.2019.00945openalex-extracted
Co-Mining: Deep Face Recognition With Noisy Labels
inferredcites →https://doi.org/10.1109/lsp.2016.2603342openalex-extracted
Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks
inferredcites →https://doi.org/10.1109/lsp.2018.2822810openalex-extracted
Additive Margin Softmax for Face Verification
inferredcites →https://doi.org/10.1109/tip.2022.3154293openalex-extracted
RVFace: Reliable Vector Guided Softmax Loss for Face Recognition
inferredcites →https://doi.org/10.1109/tmm.2021.3123478openalex-extracted
Dynamic Training Data Dropout for Robust Deep Face Recognition
inferredcites →https://doi.org/10.1109/wacv.2016.7477558openalex-extracted
Frontal to profile face verification in the wild
inferredcites →https://doi.org/10.1109/wacv.2018.00138openalex-extracted
A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels
inferredcites →https://doi.org/10.1609/aaai.v33i01.33018893openalex-extracted
Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos
inferredcites →https://doi.org/10.24963/ijcai.2018/138openalex-extracted
Ensemble Soft-Margin Softmax Loss for Image Classification
inferredcites →https://doi.org/10.48550/arxiv.1403.2802openalex-extracted
Learning Deep Face Representation
inferredcites →https://doi.org/10.48550/arxiv.1411.7923openalex-extracted
Learning Face Representation from Scratch
inferredcites →https://doi.org/10.48550/arxiv.1705.10694openalex-extracted
Deep Learning is Robust to Massive Label Noise
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.1708.08197openalex-extracted
Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments
inferredcites →https://doi.org/10.48550/arxiv.1712.05055openalex-extracted
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
inferredcites →https://doi.org/10.48550/arxiv.1803.09050openalex-extracted
Learning to Reweight Examples for Robust Deep Learning
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.1811.00116openalex-extracted
Face Recognition: From Traditional to Deep Learning Methods
inferredcites →https://doi.org/10.48550/arxiv.1812.11317openalex-extracted
Support Vector Guided Softmax Loss for Face Recognition
inferredcites →https://doi.org/10.48550/arxiv.1901.04215openalex-extracted
How does Disagreement Help Generalization against Label Corruption?
inferredcites →https://doi.org/10.48550/arxiv.1902.07379openalex-extracted
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting
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