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Enhancing Fairness of Visual Attribute Predictors

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13846))

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Abstract

The performance of deep neural networks for image recognition tasks such as predicting a smiling face is known to degrade with under-represented classes of sensitive attributes. We address this problem by introducing fairness-aware regularization losses based on batch estimates of Demographic Parity, Equalized Odds, and a novel Intersection-over-Union measure. The experiments performed on facial and medical images from CelebA, UTKFace, and the SIIM-ISIC Melanoma classification challenge show the effectiveness of our proposed fairness losses on bias mitigation as they improve model fairness while maintaining high classification performance. To the best of our knowledge, our work is the first attempt to incorporate these types of losses in an end-to-end training scheme to mitigate biases of visual attribute predictors.

Source code is available at https://github.com/nish03/FVAP.

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Notes

  1. 1.

    Strictly speaking, it directly holds only if \(p_\theta (y^*_s)\) is uniform, otherwise (3) corresponds to a squared difference between \(p_\theta (y_t,y^*_s)\) and \(p_\theta (y_t)\cdot p_\theta (y^*_s)\), where addends are additionally weighted by \(1/p_\theta (y^*_s)^2\).

  2. 2.

    We also assume that probabilities are differentiable w.r.t. parameters.

  3. 3.

    We discuss in detail only the case of binary variables and the IoU-loss for simplicity. The situation is similar for other cases.

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Acknowledgement

This work primarily received funding from the German Federal Ministry of Education and Research (BMBF) under Software Campus (grant 01IS17044) and the Competence Center for Big Data and AI ScaDS.AI Dresden/Leipzig (grant 01/S18026A-F). The work also received funding from Deutsche Forschungsgemeinschaft (DFG) (grant 389792660) as part of TRR 248 and the Cluster of Excellence CeTI (EXC2050/1, grant 390696704). The authors gratefully acknowledge the Center for Information Services and HPC (ZIH) at TU Dresden for providing computing resources.

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Authors and Affiliations

  1. Chair for Computer Graphics and Visualization, TU Dresden, Dresden, Germany

    Tobias Hänel, Nishant Kumar, Dmitrij Schlesinger, Erdem Ünal & Stefan Gumhold

  2. Carl Zeiss Meditec AG, Munich, Germany

    Mengze Li & Abouzar Eslami

Authors
  1. Tobias Hänel
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  2. Nishant Kumar
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  7. Stefan Gumhold
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Correspondence to Tobias Hänel .

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Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Hänel, T. et al. (2023). Enhancing Fairness of Visual Attribute Predictors. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13846. Springer, Cham. https://doi.org/10.1007/978-3-031-26351-4_10

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  • DOI: https://doi.org/10.1007/978-3-031-26351-4_10

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