One-line-of-code data mollification improves optimization of likelihood-based generative models

Tran, Ba-Hien; Franzese, Giulio; Michiardi, Pietro; Filippone, Maurizio
NeurIPS 2023, 37th Conference on Neural Information Processing Systems, 11-16 December 2023, New Orleans, USA

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-theart 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. 


HAL
Type:
Conference
City:
New Orleans
Date:
2023-12-11
Department:
Data Science
Eurecom Ref:
7320
Copyright:
© NIST. Personal use of this material is permitted. The definitive version of this paper was published in NeurIPS 2023, 37th Conference on Neural Information Processing Systems, 11-16 December 2023, New Orleans, USA and is available at :

PERMALINK : https://www.eurecom.fr/publication/7320