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

• URL: http://arxiv.org/abs/2402.04355v1
• Date: Tue, 6 Feb 2024 19:39:26 GMT
• Title: PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation
• Authors: Pablo Lemos, Sammy Sharief, Nikolay Malkin, Laurence Perreault-Levasseur, Yashar Hezaveh
• Abstract summary: We propose a comprehensive sample-based method for assessing the quality of generative models. The proposed approach enables the estimation of the probability that two sets of samples are drawn from the same distribution.
• Score: 8.527898482146103
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a comprehensive sample-based method for assessing the quality of generative models. The proposed approach enables the estimation of the probability that two sets of samples are drawn from the same distribution, providing a statistically rigorous method for assessing the performance of a single generative model or the comparison of multiple competing models trained on the same dataset. This comparison can be conducted by dividing the space into non-overlapping regions and comparing the number of data samples in each region. The method only requires samples from the generative model and the test data. It is capable of functioning directly on high-dimensional data, obviating the need for dimensionality reduction. Significantly, the proposed method does not depend on assumptions regarding the density of the true distribution, and it does not rely on training or fitting any auxiliary models. Instead, it focuses on approximating the integral of the density (probability mass) across various sub-regions within the data space.

Related papers

• Theory on Score-Mismatched Diffusion Models and Zero-Shot Conditional Samplers [49.97755400231656]
  We present the first performance guarantee with explicit dimensional general score-mismatched diffusion samplers. We show that score mismatches result in an distributional bias between the target and sampling distributions, proportional to the accumulated mismatch between the target and training distributions. This result can be directly applied to zero-shot conditional samplers for any conditional model, irrespective of measurement noise.
  arXiv  Detail & Related papers  (2024-10-17T16:42:12Z)
• Empirical Density Estimation based on Spline Quasi-Interpolation with applications to Copulas clustering modeling [0.0]
  Density estimation is a fundamental technique employed in various fields to model and to understand the underlying distribution of data. In this paper we propose the mono-variate approximation of the density using quasi-interpolation. The presented algorithm is validated on artificial and real datasets.
  arXiv  Detail & Related papers  (2024-02-18T11:49:38Z)
• Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data [68.62134204367668]
  This paper studies score approximation, estimation, and distribution recovery of diffusion models, when data are supported on an unknown low-dimensional linear subspace. We show that with a properly chosen neural network architecture, the score function can be both accurately approximated and efficiently estimated. The generated distribution based on the estimated score function captures the data geometric structures and converges to a close vicinity of the data distribution.
  arXiv  Detail & Related papers  (2023-02-14T17:02:35Z)
• Unsupervised Learning of Sampling Distributions for Particle Filters [80.6716888175925]
  We put forward four methods for learning sampling distributions from observed measurements. Experiments demonstrate that learned sampling distributions exhibit better performance than designed, minimum-degeneracy sampling distributions.
  arXiv  Detail & Related papers  (2023-02-02T15:50:21Z)
• BRIO: Bringing Order to Abstractive Summarization [107.97378285293507]
  We propose a novel training paradigm which assumes a non-deterministic distribution. Our method achieves a new state-of-the-art result on the CNN/DailyMail (47.78 ROUGE-1) and XSum (49.07 ROUGE-1) datasets.
  arXiv  Detail & Related papers  (2022-03-31T05:19:38Z)
• Sampling from Arbitrary Functions via PSD Models [55.41644538483948]
  We take a two-step approach by first modeling the probability distribution and then sampling from that model. We show that these models can approximate a large class of densities concisely using few evaluations, and present a simple algorithm to effectively sample from these models.
  arXiv  Detail & Related papers  (2021-10-20T12:25:22Z)
• A likelihood approach to nonparametric estimation of a singular distribution using deep generative models [4.329951775163721]
  We investigate a likelihood approach to nonparametric estimation of a singular distribution using deep generative models. We prove that a novel and effective solution exists by perturbing the data with an instance noise. We also characterize the class of distributions that can be efficiently estimated via deep generative models.
  arXiv  Detail & Related papers  (2021-05-09T23:13:58Z)
• A similarity-based Bayesian mixture-of-experts model [0.5156484100374058]
  We present a new non-parametric mixture-of-experts model for multivariate regression problems. Using a conditionally specified model, predictions for out-of-sample inputs are based on similarities to each observed data point. Posterior inference is performed on the parameters of the mixture as well as the distance metric.
  arXiv  Detail & Related papers  (2020-12-03T18:08:30Z)
• The UU-test for Statistical Modeling of Unimodal Data [0.20305676256390928]
  We propose a technique called UU-test (Unimodal Uniform test) to decide on the unimodality of a one-dimensional dataset. A unique feature of this approach is that in the case of unimodality, it also provides a statistical model of the data in the form of a Uniform Mixture Model.
  arXiv  Detail & Related papers  (2020-08-28T08:34:28Z)
• Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation [51.091890311312085]
  We propose a new training scheme for auto-regressive sequence generative models, which is effective and stable when operating at large sample space encountered in text generation. Our method stably outperforms Maximum Likelihood Estimation and other state-of-the-art sequence generative models in terms of both quality and diversity.
  arXiv  Detail & Related papers  (2020-07-12T15:31:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.