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Collaborative Discrimination-Enabled Generative Adversarial Network (CoD-GAN) for the Data Augmentation in Imbalanced Classification

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)(2022)10.1109/case49997.2022.9926707Source: DataRank Database

Collaborative Discrimination-Enabled Generative Adversarial Network (CoD-GAN) for the Data Augmentation in Imbalanced Classification is a research paper published in 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) (2022). On theSindex it has a DataRank of 0. It has been cited 2 times.

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2 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Impact (FWCI 0.83)OA: closedTopics (16)IDs (PubMed, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (0/2)

Interoperable (0/2)

Reusable (0/3)

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Authors (3)

Yuxuan Li ORCID,Chenang Liu ORCID,Ziyang Zhang ORCID

Live enrichment

PubMed:19767382 OpenAlex:https://openalex.org/W4312747005

FWCI

0.83

Citation Percentile

0.7%

Citation Trend

2024

2025

closedLicense: https://doi.org/10.15223/policy-029

publisherhttp://xplorestaging.ieee.org/ielx7/9926286/9926419/09926707.pdf?arnumber=9926707

conferencehttps://doi.org/10.1109/case49997.2022.9926707

Fields of Study

Industrial Vision Systems and Defect DetectionAnomaly Detection Techniques and ApplicationsMachine Fault Diagnosis Techniques

Keywords

Robustness (evolution)Computer scienceGenerative adversarial networkGenerative grammarAdversarial systemMachine learningTraining setArtificial intelligenceData modelingData miningPattern recognition (psychology)Deep learningDatabase

Sustainable Development Goals

SDG 0: Reduced inequalities

Collaborative Discrimination-Enabled Generative Adversarial Network (CoD-GAN) for the Data Augmentation in Imbalanced Classification

FAIR Checklist

Authors (3)

Impact

Open Access

Topics