Optimal Budgeted Rejection Sampling for Generative Models

• URL: http://arxiv.org/abs/2311.00460v2
• Date: Fri, 1 Mar 2024 10:56:19 GMT
• Title: Optimal Budgeted Rejection Sampling for Generative Models
• Authors: Alexandre Verine and Muni Sreenivas Pydi and Benjamin Negrevergne and Yann Chevaleyre
• Abstract summary: Rejection sampling methods have been proposed to improve the performance of discriminator-based generative models. We first propose an Optimal Budgeted Rejection Sampling scheme that is provably optimal. Second, we propose an end-to-end method that incorporates the sampling scheme into the training procedure to further enhance the model's overall performance.
• Score: 54.050498411883495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rejection sampling methods have recently been proposed to improve the performance of discriminator-based generative models. However, these methods are only optimal under an unlimited sampling budget, and are usually applied to a generator trained independently of the rejection procedure. We first propose an Optimal Budgeted Rejection Sampling (OBRS) scheme that is provably optimal with respect to \textit{any} $f$-divergence between the true distribution and the post-rejection distribution, for a given sampling budget. Second, we propose an end-to-end method that incorporates the sampling scheme into the training procedure to further enhance the model's overall performance. Through experiments and supporting theory, we show that the proposed methods are effective in significantly improving the quality and diversity of the samples.

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