Summary of A Practical Guide to Sample-based Statistical Distances For Evaluating Generative Models in Science, by Sebastian Bischoff et al.
A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Science
by Sebastian Bischoff, Alana Darcher, Michael Deistler, Richard Gao, Franziska Gerken, Manuel Gloeckler, Lisa Haxel, Jaivardhan Kapoor, Janne K Lappalainen, Jakob H Macke, Guy Moss, Matthijs Pals, Felix Pei, Rachel Rapp, A Erdem Sağtekin, Cornelius Schröder, Auguste Schulz, Zinovia Stefanidi, Shoji Toyota, Linda Ulmer, Julius Vetter
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper provides an accessible introduction to understanding popular sample-based statistical distances used to evaluate the output of generative models. The authors focus on four commonly used notions: Sliced-Wasserstein (SW), Classifier Two-Sample Tests (C2ST), Maximum Mean Discrepancy (MMD), and Fréchet Inception Distance (FID). Each distance is explained with its intuition, merits, scalability, complexity, and pitfalls. The authors demonstrate the application of these distances in evaluating generative models from different scientific domains, including a decision-making model and a medical image generation model. This study highlights that distinct distances can produce different results on similar data. By providing an intuitive guide to understanding statistical distances, this paper aims to help researchers interpret and evaluate the output of generative models. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to measure the quality of things generated by computers, like images or decisions. It explains four ways to compare two groups of data: sliced-Wasserstein, classifier tests, maximum mean discrepancy, and Fréchet inception distance. Each method has its own strengths and weaknesses. The authors show how these methods can be used in real-life situations, such as evaluating models that generate medical images or make decisions. They also demonstrate that different methods can give different answers when comparing the same data. By making these statistical distances more accessible, this paper aims to help researchers understand and use them correctly. |
Keywords
* Artificial intelligence * Image generation