Summary of Discover and Mitigate Multiple Biased Subgroups in Image Classifiers, by Zeliang Zhang et al.
Discover and Mitigate Multiple Biased Subgroups in Image Classifiers
by Zeliang Zhang, Mingqian Feng, Zhiheng Li, Chenliang Xu
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning models excel on familiar data but struggle when applied to diverse and underrepresented groups, jeopardizing their reliability. The key to improving these models lies in identifying the hidden biases that cause them to fail. Most existing approaches assume that models only falter due to a single bias, which is not the case in real-world scenarios where multiple biases coexist. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are really good at doing things we teach them to do, but they often struggle when dealing with people or groups that are different from what they’ve learned. This can be a big problem because it means they might not work well for everyone. To make these models better, we need to find the hidden reasons why they’re not working and improve their performance. |
Keywords
» Artificial intelligence » Machine learning
