Summary of Measuring Diversity: Axioms and Challenges, by Mikhail Mironov and Liudmila Prokhorenkova
Measuring Diversity: Axioms and Challenges
by Mikhail Mironov, Liudmila Prokhorenkova
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A new paper tackles the challenge of quantifying diversity in various applications like image generation and recommender systems. The authors first conduct a thorough review of existing diversity measures, highlighting their limitations and undesirable behaviors. Based on this analysis, they formulate three desirable properties (axioms) for a reliable diversity measure: monotonicity, uniqueness, and continuity. Unfortunately, none of the current measures satisfy all these axioms, making them unsuitable for measuring diversity. To address this gap, the authors propose two alternative measures that meet the axioms, but note that they are computationally complex and not practical for real-world use. As a result, an open problem is posed to construct a diversity measure that balances theoretical requirements with computational feasibility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Diversity is important in many areas like image generation or recommending products. The problem is that we don’t have good ways to measure it. This paper looks at what’s already been tried and finds that most methods are not very good because they don’t meet certain rules (axioms). The authors then create two new methods that do meet the axioms, but these methods are too complicated for real-world use. So, the challenge remains to find a way to measure diversity that is both mathematically sound and easy to compute. |
Keywords
» Artificial intelligence » Image generation
