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Revisiting the Importance of Amplifying Bias for Debiasing
2 references·1 citing·14 second hop
Revisiting the Importance of Amplifying Bias for Debiasing (2023)Revisiting the Importance of Amplifying B…EnD: Entangling and Disentangling deep representations for bias correction (2021)EnD: Entangling and Disentang…Learning from Failure: Training Debiased Classifier from Biased Classifier (2020)Learning from Failure: Traini…FLAC: Fairness-Aware Representation Learning by Suppressing Attribute-Class Associations (2024)FLAC: Fairness-Aware Represen…The Filter Bubble: What the Internet Is Hiding from You (2011)REPAIR: Removing Representation Bias by Dataset Resampling (2019)A Survey on Bias and Fairness in Machine Learning (2021)Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization (2021)Unsupervised Learning of Debiased Representations with Pseudo-Attributes (2022)Overwriting Pretrained Bias with Finetuning Data (2023)Women Also Snowboard: Overcoming Bias in Captioning Models (2018)Artificial Intelligence: American Attitudes and Trends (2019)Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations (2019)Bias Mimicking: A Simple Sampling Approach for Bias Mitigation (2023)BiasAdv: Bias-Adversarial Augmentation for Model Debiasing (2023)Fairness in Deep Learning: A Survey on Vision and Language Research (2023)FairGAN: Fairness-aware Generative Adversarial Networks (2018)Bias in data‐driven artificial intelligence systems—An introductory survey (2020)
this work references cited by second hop

References

  • 2021EnD: Entangling and Disentangling deep representations for bias correction83 cited
  • 2020Learning from Failure: Training Debiased Classifier from Biased Classifier58 cited

Cited by

  • 2024FLAC: Fairness-Aware Representation Learning by Suppressing Attribute-Class Associations15 cited

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