Summary of Examining Pathological Bias in a Generative Adversarial Network Discriminator: a Case Study on a Stylegan3 Model, by Alvin Grissom Ii et al.
Examining Pathological Bias in a Generative Adversarial Network Discriminator: A Case Study on a StyleGAN3 Model
by Alvin Grissom II, Ryan F. Lei, Matt Gusdorff, Jeova Farias Sales Rocha Neto, Bailey Lin, Ryan Trotter
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates biases in generative adversarial networks (GANs) specifically targeting the discriminator of a pre-trained StyleGAN3-r model. The authors uncover internal color and luminance biases that are not attributed to training data, revealing a pathological issue. Furthermore, they demonstrate that the discriminator prioritizes scores based on both image- and face-level qualities, resulting in disproportionate effects across categories like gender, race, and more. By examining common axes from stereotyping research in social psychology, this study highlights the need for a deeper understanding of biases within machine learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how artificial intelligence (AI) can make mistakes by being biased towards certain groups or things. The authors took a special kind of AI called a generative adversarial network and found that it has internal problems that affect what images it creates. They also discovered that this AI is more likely to like certain types of faces over others, which is not fair. To understand why this happens, the researchers looked at how people often make judgments about each other based on things like gender or race. |
Keywords
* Artificial intelligence * Generative adversarial network * Machine learning
