Benchmarking the Fairness of Image Upsampling Methods-Reference-Cited by-同舟云学术

Benchmarking the Fairness of Image Upsampling Methods

Published:2024-06-03 Issue: Volume: Page:489-517
ISSN:
Container-title:The 2024 ACM Conference on Fairness, Accountability, and Transparency
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Author:

Laszkiewicz Mike¹^ORCID,Daunhawer Imant²^ORCID,Vogt Julia E.²^ORCID,Fischer Asja¹^ORCID,Lederer Johannes³^ORCID

Affiliation:

1. Faculty of Computer Science, Ruhr University Bochum, Germany

2. Department of Computer Science, ETH Zurich, Switzerland

3. Department of Mathematics, Computer Science, and Natural Sciences, University of Hamburg, Germany

Funder

Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany?s Excellence Strategy

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3630106.3658921

Reference71 articles.

1. Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna M. Wallach. 2018. A Reductions Approach to Fair Classification. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018(Proceedings of Machine Learning Research, Vol. 80), Jennifer G. Dy and Andreas Krause (Eds.). PMLR, 60–69. http://proceedings.mlr.press/v80/agarwal18a.html

2. A Test of Goodness of Fit

3. Carlotta Balestra and Lara Fleischer. 2018. Diversity statistics in the OECD. (2018). https://doi.org/https://doi.org/10.1787/89bae654-en

4. Mislav Balunovic, Anian Ruoss, and Martin T. Vechev. 2022. Fair Normalizing Flows. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. https://openreview.net/forum?id=BrFIKuxrZE

5. Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2023. Fairness and Machine Learning: Limitations and Opportunities. MIT Press.