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Conditional Adversarial Debiasing: Towards Learning Unbiased Classifiers from Biased Data · Study

WorkConditional Adversarial Debiasing: Towards Learning Unbiased Classifiers from Biased Data

Christian Reimers2021

Work2021

Conditional Adversarial Debiasing: Towards Learning Unbiased Classifiers from Biased Data

Christian Reimers · Paul Bodesheim · Jakob Runge · Joachim Denzler

10 citations·0/0 files stored·Lecture notes in computer science

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Ethics and Social Impacts of AI Domain Adaptation and Few-Shot Learning Machine Learning and Data Classification

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References

2018Mitigating Unwanted Biases with Adversarial Learning 2019Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings 2021Representation Learning with Statistical Independence to Mitigate Bias 2020REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets 2020An Enhanced Deep Neural Network for Predicting Workplace Absenteeism

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Year2021

Typeother

Published2021-01-01

VenueLecture notes in computer science

ISSN-L0302-9743

Languageen

Citations10

Files0 public / 0

ShelfEthics and Social Impacts of AI

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doi10.1007/978-3-030-92659-5_4

openalex_workW4206283208

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