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  <head>
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  <meta charset="utf-8">
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  <meta name="description"
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- content="Deformable Neural Radiance Fields creates free-viewpoint portraits (nerfies) from casually captured videos.">
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- <meta name="keywords" content="Nerfies, D-NeRF, NeRF">
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  <meta name="viewport" content="width=device-width, initial-scale=1">
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- <title>Nerfies: Deformable Neural Radiance Fields</title>
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  <link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
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  rel="stylesheet">
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  <div class="container is-max-desktop">
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  <div class="columns is-centered">
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  <div class="column has-text-centered">
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- <h1 class="title is-1 publication-title">Nerfies: Deformable Neural Radiance Fields</h1>
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  <div class="is-size-5 publication-authors">
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  <span class="author-block">
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- <a href="https://keunhong.com" target="_blank">Keunhong Park</a><sup>1</sup>,</span>
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  <span class="author-block">
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- <a href="https://utkarshsinha.com" target="_blank">Utkarsh Sinha</a><sup>2</sup>,</span>
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  <span class="author-block">
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- <a href="https://jonbarron.info" target="_blank">Jonathan T. Barron</a><sup>2</sup>,
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  </span>
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  <span class="author-block">
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- <a href="http://sofienbouaziz.com" target="_blank">Sofien Bouaziz</a><sup>2</sup>,
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  </span>
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  <span class="author-block">
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- <a href="https://www.danbgoldman.com" target="_blank">Dan B Goldman</a><sup>2</sup>,
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- </span>
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- <span class="author-block">
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- <a href="https://homes.cs.washington.edu/~seitz/" target="_blank">Steven M. Seitz</a><sup>1,2</sup>,
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- </span>
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- <span class="author-block">
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- <a href="http://www.ricardomartinbrualla.com" target="_blank">Ricardo Martin-Brualla</a><sup>2</sup>
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  </span>
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  </div>
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  <div class="is-size-5 publication-authors">
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- <span class="author-block"><sup>1</sup>University of Washington,</span>
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- <span class="author-block"><sup>2</sup>Google Research</span>
 
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  </div>
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  <div class="column has-text-centered">
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  <span>arXiv</span>
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  </a>
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  </span>
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- <!-- Video Link. -->
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- <span class="link-block">
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- <a href="https://www.youtube.com/watch?v=MrKrnHhk8IA" target="_blank"
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- class="external-link button is-normal is-rounded is-dark">
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- <span class="icon">
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- <i class="fab fa-youtube"></i>
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- </span>
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- <span>Video</span>
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- </a>
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- </span>
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  <!-- Code Link. -->
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  <span class="link-block">
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  <a href="https://github.com/google/nerfies" target="_blank"
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  <span>Code</span>
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  </a>
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  </span>
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- <!-- Dataset Link. -->
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  <span class="link-block">
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  <a href="https://github.com/google/nerfies/releases/tag/0.1" target="_blank"
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  class="external-link button is-normal is-rounded is-dark">
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  <span class="icon">
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  <i class="far fa-images"></i>
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  </span>
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- <span>Data</span>
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  </a>
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  </div>
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  <h2 class="title is-3">Abstract</h2>
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  <div class="content has-text-justified">
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  <p>
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- We present the first method capable of photorealistically reconstructing a non-rigidly
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- deforming scene using photos/videos captured casually from mobile phones.
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- </p>
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- <p>
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- Our approach augments neural radiance fields
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- (NeRF) by optimizing an
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- additional continuous volumetric deformation field that warps each observed point into a
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- canonical 5D NeRF.
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- We observe that these NeRF-like deformation fields are prone to local minima, and
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- propose a coarse-to-fine optimization method for coordinate-based models that allows for
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- more robust optimization.
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- By adapting principles from geometry processing and physical simulation to NeRF-like
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- models, we propose an elastic regularization of the deformation field that further
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- improves robustness.
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- </p>
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- <p>
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- We show that <span class="dnerf">Nerfies</span> can turn casually captured selfie
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- photos/videos into deformable NeRF
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- models that allow for photorealistic renderings of the subject from arbitrary
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- viewpoints, which we dub <i>"nerfies"</i>. We evaluate our method by collecting data
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- using a
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- rig with two mobile phones that take time-synchronized photos, yielding train/validation
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- images of the same pose at different viewpoints. We show that our method faithfully
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- reconstructs non-rigidly deforming scenes and reproduces unseen views with high
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- fidelity.
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  </p>
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  </div>
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  </div>
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  </div>
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  <!--/ Abstract. -->
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-
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- <!-- Paper video. -->
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- <div class="columns is-centered has-text-centered">
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- <div class="column is-four-fifths">
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- <h2 class="title is-3">Video</h2>
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- <div class="publication-video">
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- <iframe src="https://www.youtube.com/embed/MrKrnHhk8IA?rel=0&amp;showinfo=0"
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- frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
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- </div>
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- </div>
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- </div>
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- <!--/ Paper video. -->
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- </div>
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  </section>
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  href="http://creativecommons.org/licenses/by-sa/4.0/">Creative
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  Commons Attribution-ShareAlike 4.0 International License</a>.
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  </p>
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- <p>
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- This means you are free to borrow the <a target="_blank"
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- href="https://github.com/nerfies/nerfies.github.io">source code</a> of this website,
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- we just ask that you link back to this page in the footer.
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- Please remember to remove the analytics code included in the header of the website which
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- you do not want on your website.
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- </p>
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  </div>
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  </div>
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  </div>
 
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  <head>
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  <meta charset="utf-8">
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  <meta name="description"
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+ content="Empirical Benchmarking of Algorithmic Fairness in Machine Learning Models">
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+ <meta name="keywords" content="Machine Learning, Bias Mitigation, Benchmark">
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  <meta name="viewport" content="width=device-width, initial-scale=1">
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+ <title>BMBENCH: Empirical Benchmarking of Algorithmic Fairness in Machine Learning Models</title>
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  <link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
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  rel="stylesheet">
 
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  <div class="container is-max-desktop">
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  <div class="columns is-centered">
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  <div class="column has-text-centered">
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+ <h1 class="title is-1 publication-title">BMBENCH: Empirical Benchmarking of Algorithmic Fairness in Machine Learning Models</h1>
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  <div class="is-size-5 publication-authors">
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  <span class="author-block">
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+ <a href="https://kleytondacosta.com" target="_blank">Kleyton da Costa</a><sup>1, 2</sup>,</span>
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  <span class="author-block">
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+ <a href="https://utkarshsinha.com" target="_blank">Cristian Munoz</a><sup>1</sup>,</span>
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  <span class="author-block">
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+ <a href="https://jonbarron.info" target="_blank">Bernando Modenesi</a><sup>3</sup>,
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  </span>
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  <span class="author-block">
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+ <a href="http://sofienbouaziz.com" target="_blank">Franklin Fernandez</a><sup>1,2</sup>,
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  </span>
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  <span class="author-block">
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+ <a href="https://www.danbgoldman.com" target="_blank">Adriano Koshiyama</a><sup>1</sup>,
 
 
 
 
 
 
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  </span>
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  </div>
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  <div class="is-size-5 publication-authors">
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+ <span class="author-block"><sup>1</sup>Holistic AI,</span>
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+ <span class="author-block"><sup>2</sup>Pontifical Catholic University of Utah,</span>
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+ <span class="author-block"><sup>2</sup>University of Utah,</span>
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  </div>
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  <div class="column has-text-centered">
 
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  <span>arXiv</span>
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  </a>
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  </span>
 
 
 
 
 
 
 
 
 
 
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  <!-- Code Link. -->
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  <span class="link-block">
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  <a href="https://github.com/google/nerfies" target="_blank"
 
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  <span>Code</span>
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  </a>
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  </span>
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+ <!-- Leaderboard. -->
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  <span class="link-block">
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  <a href="https://github.com/google/nerfies/releases/tag/0.1" target="_blank"
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  class="external-link button is-normal is-rounded is-dark">
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  <span class="icon">
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  <i class="far fa-images"></i>
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  </span>
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+ <span>Leaderboard</span>
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  </a>
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  </div>
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  <h2 class="title is-3">Abstract</h2>
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  <div class="content has-text-justified">
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  <p>
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+ The development and assessment of bias mitigation methods require rigorous benchmarks.
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+ This paper introduces BMBench, a comprehensive benchmarking framework to evaluate bias
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+ mitigation strategies across multitask machine learning predictions (binary classification,
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+ multiclass classification, regression, and clustering). Our benchmark leverages state-of-the-art
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+ and proposed datasets to improve fairness research, offering a broad spectrum of fairness
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+ metrics for a robust evaluation of bias mitigation methods. We provide an open-source repository
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+ to allow researchers to test and refine their bias mitigation approaches easily,
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+ promoting advancements in the creation of fair machine learning models.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  </p>
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  </div>
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  </div>
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  </div>
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  <!--/ Abstract. -->
 
 
 
 
 
 
 
 
 
 
 
 
 
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  </section>
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  href="http://creativecommons.org/licenses/by-sa/4.0/">Creative
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  Commons Attribution-ShareAlike 4.0 International License</a>.
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  </p>
 
 
 
 
 
 
 
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  </div>
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  </div>
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  </div>