One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models

• URL: http://arxiv.org/abs/2305.18900v2
• Date: Thu, 21 Dec 2023 18:22:04 GMT
• Title: One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models
• Authors: Ba-Hien Tran, Giulio Franzese, Pietro Michiardi, Maurizio Filippone
• Abstract summary: Likelihood-based GMs are attractive due to the possibility to generate new data by a single model evaluation. They typically achieve lower sample quality compared to state-of-the-art score-based diffusion models (DMs)
• Score: 17.47235124122244
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative Models (GMs) have attracted considerable attention due to their tremendous success in various domains, such as computer vision where they are capable to generate impressive realistic-looking images. Likelihood-based GMs are attractive due to the possibility to generate new data by a single model evaluation. However, they typically achieve lower sample quality compared to state-of-the-art score-based diffusion models (DMs). This paper provides a significant step in the direction of addressing this limitation. The idea is to borrow one of the strengths of score-based DMs, which is the ability to perform accurate density estimation in low-density regions and to address manifold overfitting by means of data mollification. We connect data mollification through the addition of Gaussian noise to Gaussian homotopy, which is a well-known technique to improve optimization. Data mollification can be implemented by adding one line of code in the optimization loop, and we demonstrate that this provides a boost in generation quality of likelihood-based GMs, without computational overheads. We report results on image data sets with popular likelihood-based GMs, including variants of variational autoencoders and normalizing flows, showing large improvements in FID score.

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