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FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis · Study

WorkFairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis

Yawen Wu2022

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Workgreen2022

FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis

Yawen Wu · Xiaowei Xu · Yiyu Shi · Jingtong Hu

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

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Ethics and Social Impacts of AI Advanced Neural Network Applications Explainable Artificial Intelligence (XAI)

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References

2018Mitigating Unwanted Biases with Adversarial Learning 2011Data preprocessing techniques for classification without discrimination 2017Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints 2018Women Also Snowboard: Overcoming Bias in Captioning Models 2019Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations 2020Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation 2018Adversarial Removal of Demographic Attributes from Text Data 2019Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings

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2023Improving model fairness in image-based computer-aided diagnosis 2023FairAdaBN: Mitigating Unfairness with Adaptive Batch Normalization and Its Application to Dermatological Disease Classification

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Year2022

Typeother

Published2022-01-01

VenueLecture notes in computer science

ISSN-L0302-9743

Languageen

Citations50

Files0 public / 0

ShelfEthics and Social Impacts of AI

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doi10.1007/978-3-031-16431-6_70

openalex_workW4295795871

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