Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach
Giorgio Patrini low, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, Lizhen Qu
The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.
this paper
works it cites
works citing it
node size = global citations · hover for the full title
Citation edge evidence
- Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels→Making Deep Neural Networks Robust to Label Noise: A Loss Correction ApproachLegacy edge — source detail unavailable
- Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach→Long Short-Term MemoryLegacy edge — source detail unavailable
- Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
- Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach→Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…Legacy edge — source detail unavailable
- Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach→Understanding the difficulty of training deep feedforward neural networksLegacy edge — source detail unavailable
- Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach→Adaptive Subgradient Methods for Online Learning and Stochastic OptimizationLegacy edge — source detail unavailable
- Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach→Learning Word Vectors for Sentiment AnalysisLegacy edge — source detail unavailable
- Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach→Classification in the Presence of Label Noise: A SurveyLegacy edge — source detail unavailable
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…→Understanding the difficulty of training deep feedforward neural networksLegacy edge — source detail unavailable
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…→Adaptive Subgradient Methods for Online Learning and Stochastic OptimizationLegacy edge — source detail unavailable
What this paper cites, inside the corpus
What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels | 2018 | 1,477 |
Links
Topics
| Machine Learning and Data Classification | Computer Science |
| Advanced Neural Network Applications | Computer Science |
| Adversarial Robustness in Machine Learning | Computer Science |
Is this record sound?
complete
Nothing in this record contradicts itself and no field we check is missing.
- supports5 author record(s) attached.
- supports71 reference(s) recorded.
- supportsThe DOI's year agrees with the publication year.
- supportsA title is present.
Provenance
sha256 88a60cdbb94c6a13…