BMBENCH: Empirical Benchmarking of Algorithmic Fairness in Machine Learning Models

Kleyton da Costa1, 2, Cristian Munoz1, Bernando Modenesi3, Franklin Fernandez1,2, Adriano Koshiyama1,
1Holistic AI, 2Pontifical Catholic University of Rio de Janeiro, 2University of Utah,
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Nerfies turns selfie videos from your phone into free-viewpoint portraits.

Abstract

The development and assessment of bias mitigation methods require rigorous benchmarks. This paper introduces BMBench, a comprehensive benchmarking framework to evaluate bias mitigation strategies across multitask machine learning predictions (binary classification, multiclass classification, regression, and clustering). Our benchmark leverages state-of-the-art and proposed datasets to improve fairness research, offering a broad spectrum of fairness metrics for a robust evaluation of bias mitigation methods. We provide an open-source repository to allow researchers to test and refine their bias mitigation approaches easily, promoting advancements in the creation of fair machine learning models.

Visual Effects

Using nerfies you can create fun visual effects. This Dolly zoom effect would be impossible without nerfies since it would require going through a wall.

Matting

As a byproduct of our method, we can also solve the matting problem by ignoring samples that fall outside of a bounding box during rendering.

Animation

Interpolating states

We can also animate the scene by interpolating the deformation latent codes of two input frames. Use the slider here to linearly interpolate between the left frame and the right frame.

Interpolate start reference image.

Start Frame

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Interpolation end reference image.

End Frame


Re-rendering the input video

Using Nerfies, you can re-render a video from a novel viewpoint such as a stabilized camera by playing back the training deformations.

Related Links

There's a lot of excellent work that was introduced around the same time as ours.

Progressive Encoding for Neural Optimization introduces an idea similar to our windowed position encoding for coarse-to-fine optimization.

D-NeRF and NR-NeRF both use deformation fields to model non-rigid scenes.

Some works model videos with a NeRF by directly modulating the density, such as Video-NeRF, NSFF, and DyNeRF

There are probably many more by the time you are reading this. Check out Frank Dellart's survey on recent NeRF papers, and Yen-Chen Lin's curated list of NeRF papers.

BibTeX

@article{park2021nerfies,
  author    = {Park, Keunhong and Sinha, Utkarsh and Barron, Jonathan T. and Bouaziz, Sofien and Goldman, Dan B and Seitz, Steven M. and Martin-Brualla, Ricardo},
  title     = {Nerfies: Deformable Neural Radiance Fields},
  journal   = {ICCV},
  year      = {2021},
}