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

With advancements in generative models, evaluating their performance using rigorous, clearly defined metrics and We propose a comprehensive sample-based criteria has become increasingly essential. Disambiguating method for assessing the quality of generative true from modeled distributions is especially pertinent in models. The proposed approach enables the estimation light of the growing emphasis on AI safety within the community, of the probability that two sets of samples as well as in scientific domains where stringent standards are drawn from the same distribution, providing of rigor and uncertainty quantification are needed for a statistically rigorous method for assessing the the adoption of machine learning methods. When evaluating performance of a single generative model or the generative models, we are interested in three qualitative comparison of multiple competing models trained properties (Stein et al., 2023; Jiralerspong et al., 2023): Fidelity on the same dataset. This comparison can be conducted refers to the quality and realism of individual outputs by dividing the space into non-overlapping generated by a model. It assesses how indistinguishable regions and comparing the number of data samples each generated sample is from real data.