On the public citation graph

https://doi.org/10.2139/ssrn.4656599

0 signed · 73 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

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

inferredcites →https://doi.org/10.1007/978-3-030-58517-4_46openalex-extracted
Suppressing Mislabeled Data via Grouping and Self-attention
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/10.1007/978-3-7908-2604-3_16openalex-extracted
Large-Scale Machine Learning with Stochastic Gradient Descent
inferredcites →https://doi.org/10.1016/j.compeleceng.2022.108457openalex-extracted
A model with pseudo-label correction and distribution alignment for image clustering
inferredcites →https://doi.org/10.1016/j.compeleceng.2023.109029openalex-extracted
Unsupervised multi-source domain adaptation for person re-identification via feature fusion and pseudo-label refinement
inferredcites →https://doi.org/10.1016/j.patcog.2005.10.028openalex-extracted
A study of Gaussian mixture models of color and texture features for image classification and segmentation
inferredcites →https://doi.org/10.1109/18.61115openalex-extracted
Divergence measures based on the Shannon entropy
inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-extracted
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/cvpr.2015.7298885openalex-extracted
Learning from massive noisy labeled data for image classification
inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-extracted
Rethinking the Inception Architecture for Computer Vision
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-extracted
Deep Residual Learning for Image Recognition
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.2018.00582openalex-extracted
Joint Optimization Framework for Learning with Noisy Labels
inferredcites →https://doi.org/10.1109/cvpr.2018.00906openalex-extracted
Iterative Learning with Open-set Noisy Labels
inferredcites →https://doi.org/10.1109/cvpr.2019.00718openalex-extracted
Probabilistic End-To-End Noise Correction for Learning With 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/cvpr46437.2021.00515openalex-extracted
Jo-SRC: A Contrastive Approach for Combating Noisy Labels
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/cvpr52688.2022.00041openalex-extracted
Selective-Supervised Contrastive Learning with Noisy Labels
inferredcites →https://doi.org/10.1109/iccv.2017.211openalex-extracted
Learning from Noisy Labels with Distillation
inferredcites →https://doi.org/10.1109/iccv.2019.00041openalex-extracted
Symmetric Cross Entropy for Robust Learning With Noisy Labels
inferredcites →https://doi.org/10.1109/iccv48922.2021.01043openalex-extracted
Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach
inferredcites →https://doi.org/10.1109/iccvw.2013.77openalex-extracted
3D Object Representations for Fine-Grained Categorization
inferredcites →https://doi.org/10.1109/tkde.2019.2903036openalex-extracted
Towards Automatic Construction of Diverse, High-Quality Image Datasets
inferredcites →https://doi.org/10.1109/tmm.2021.3055024openalex-extracted
Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Open-Set Noise and Utilizing Hard Examples
inferredcites →https://doi.org/10.1109/tmm.2021.3134156openalex-extracted
Robust Learning From Noisy Web Images Via Data Purification for Fine-Grained Recognition
inferredcites →https://doi.org/10.1109/tmm.2022.3181439openalex-extracted
Guided by Meta-Set: A Data-Driven Method for Fine-Grained Visual Recognition
inferredcites →https://doi.org/10.1109/tnnls.2020.2966644openalex-extracted
Exploiting Web Images for Multi-Output Classification: From Category to Subcategories
inferredcites →https://doi.org/10.1109/tpami.2015.2456899openalex-extracted
Classification with Noisy Labels by Importance Reweighting
inferredcites →https://doi.org/10.1109/tpami.2023.3271451openalex-extracted
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning
inferredcites →https://doi.org/10.1109/wacv48630.2021.00365openalex-extracted
EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels
inferredcites →https://doi.org/10.1145/3394171.3413978openalex-extracted
CRSSC: Salvage Reusable Samples from Noisy Data for Robust Learning
inferredcites →https://doi.org/10.1145/3394171.3414044openalex-extracted
Data-driven Meta-set Based Fine-Grained Visual Recognition
inferredcites →https://doi.org/10.1201/b11429-4openalex-extracted
Large-Scale Machine Learning with Stochastic Gradient Descent
inferredcites →https://doi.org/10.1609/aaai.v31i1.10894openalex-extracted
Robust Loss Functions under Label Noise for Deep Neural Networks
inferredcites →https://doi.org/10.1609/aaai.v34i07.6973openalex-extracted
Web-Supervised Network with Softly Update-Drop Training for Fine-Grained Visual Classification
inferredcites →https://doi.org/10.1609/aaai.v35i10.17041openalex-extracted
From Label Smoothing to Label Relaxation
inferredcites →https://doi.org/10.24963/ijcai.2019/140openalex-extracted
Dynamically Visual Disambiguation of Keyword-based Image Search
inferredcites →https://doi.org/10.24963/ijcai.2021/340openalex-extracted
Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion
inferredcites →https://doi.org/10.24963/ijcai.2022/455openalex-extracted
SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.1306.5151openalex-extracted
Fine-Grained Visual Classification of Aircraft
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-extracted
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1609.03683openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1609.08675openalex-extracted
YouTube-8M: A Large-Scale Video Classification Benchmark
inferredcites →https://doi.org/10.48550/arxiv.1610.02136openalex-extracted
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1611.03530openalex-extracted
Understanding deep learning requires rethinking generalization
inferredcites →https://doi.org/10.48550/arxiv.1704.07433openalex-extracted
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples
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.1706.05394openalex-extracted
A Closer Look at Memorization in Deep Networks
inferredcites →https://doi.org/10.48550/arxiv.1712.09482openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1802.05300openalex-extracted
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
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.1803.11364openalex-extracted
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.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
inferredcites →https://doi.org/10.48550/arxiv.1903.07788openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1905.02249openalex-extracted
MixMatch: A Holistic Approach to Semi-Supervised Learning
inferredcites →https://doi.org/10.48550/arxiv.1908.06112openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2002.10319openalex-extracted
Self-Adaptive Training: beyond Empirical Risk Minimization
inferredcites →https://doi.org/10.48550/arxiv.2003.02752openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2006.13554openalex-extracted
Normalized Loss Functions for Deep Learning with Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.2009.07995openalex-extracted
MoPro: Webly Supervised Learning with Momentum Prototypes
inferredcites →https://doi.org/10.48550/arxiv.2103.07756openalex-extracted
Learning with Feature-Dependent Label Noise: A Progressive Approach
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.2202.05613openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2203.04181openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2205.01156openalex-extracted
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/i9oeewf6openalex-extracted
Caltech-UCSD Birds-200-2011 Dataset
inferredcites →https://doi.org/10.57702/zp44cu3gopenalex-extracted
Learning Multiple Layers of Features from Tiny Images
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.