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PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation

Created by
  • Haebom

Author

Pablo Lemos, Sammy Sharief, Nikolay Malkin, Salma Salhi, Connor Stone, Laurence Perreault-Levasseur, Yashar Hezaveh

Outline

This paper proposes a likelihood-free method for comparing two distributions, given samples drawn from both distributions, with the goal of assessing the quality of generative models. The proposed method, PQMass, provides a statistically rigorous method for evaluating the performance of a single generative model or comparing multiple competing models. PQMass divides the sample space into non-overlapping regions and applies a chi-square test to the number of data samples in each region. This produces a p-value, which measures the probability that the coefficients of the binomial distribution derived from two sets of samples are drawn from the same multinomial distribution. PQMass does not rely on assumptions about the density of the true distribution or on the training or fitting of auxiliary models. We evaluate PQMass on data of various modes and dimensions, demonstrating its effectiveness in assessing the quality, novelty, and diversity of generated samples. Furthermore, we demonstrate that PQMass scales well to moderately high-dimensional data, suggesting that feature extraction is unnecessary in practical applications.

Takeaways, Limitations

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Takeaways:
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We present a novel approach to generative model evaluation by enabling comparison of two distributions in a likelihood-free manner.
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It is applicable to data of various modes and dimensions, and is useful for practical applications because it does not require feature extraction.
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It allows for the evaluation of a single model as well as comparison of multiple competing models.
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Provides statistically rigorous p-values to quantitatively evaluate model performance.
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Limitations:
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Scalability to high-dimensional data may be moderately limited (referred to as "moderately high-dimensional" in the paper).
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Performance may be affected by the region partitioning strategy. (While the paper does not mention a specific region partitioning strategy, it suggests that further research may be needed.)
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Caution is required when interpreting p-values. A small p-value does not necessarily mean the two distributions are different. (It's important to distinguish between statistical significance and substantive significance.)
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