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.

Related papers

• Don't be so negative! Score-based Generative Modeling with Oracle-assisted Guidance [12.039478020062608]
  We develop a new denoising diffusion probabilistic modeling (DDPM) methodology, Gen-neG. Our approach builds on generative adversarial networks (GANs) and discriminator guidance in diffusion models to guide the generation process. We empirically establish the utility of Gen-neG in applications including collision avoidance in self-driving simulators and safety-guarded human motion generation.
  arXiv  Detail & Related papers  (2023-07-31T07:52:00Z)
• Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows [54.050498411883495]
  We develop a novel training method for generative models, such as Generative Adversarial Networks and Normalizing Flows. We show that achieving a specified precision-recall trade-off corresponds to minimizing a unique $f$-divergence from a family we call the textitPR-divergences. Our approach improves the performance of existing state-of-the-art models like BigGAN in terms of either precision or recall when tested on datasets such as ImageNet.
  arXiv  Detail & Related papers  (2023-05-30T10:07:17Z)
• Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
  We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
  arXiv  Detail & Related papers  (2023-05-01T20:04:46Z)
• Exponentially Tilted Gaussian Prior for Variational Autoencoder [3.52359746858894]
  Recent studies show that probabilistic generative models can perform poorly on this task. We propose the exponentially tilted Gaussian prior distribution for the Variational Autoencoder (VAE) We show that our model produces high quality image samples which are more crisp than that of a standard Gaussian VAE.
  arXiv  Detail & Related papers  (2021-11-30T18:28:19Z)
• Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion [1.2599533416395767]
  Models of human motion commonly focus either on trajectory prediction or action classification but rarely both. Here we propose a novel architecture based on hierarchical variational autoencoders and deep graph convolutional neural networks for generating a holistic model of action over multiple time-scales. We show this Hierarchical Graph-conational Varivolutional Autoencoder (HG-VAE) to be capable of generating coherent actions, detecting out-of-distribution data, and imputing missing data by gradient ascent on the model's posterior.
  arXiv  Detail & Related papers  (2021-11-24T16:21:07Z)
• Cauchy-Schwarz Regularized Autoencoder [68.80569889599434]
  Variational autoencoders (VAE) are a powerful and widely-used class of generative models. We introduce a new constrained objective based on the Cauchy-Schwarz divergence, which can be computed analytically for GMMs. Our objective improves upon variational auto-encoding models in density estimation, unsupervised clustering, semi-supervised learning, and face analysis.
  arXiv  Detail & Related papers  (2021-01-06T17:36:26Z)
• Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More [6.89001867562902]
  Max-Mahalanobis (MMC) can be trained discriminatively, generatively, or jointly for image classification and generation. We show that our Generative MMC (GMMC) can be trained discriminatively, generatively, or jointly for image classification and generation.
  arXiv  Detail & Related papers  (2021-01-01T00:42:04Z)
• Improving Generative Adversarial Networks with Local Coordinate Coding [150.24880482480455]
  Generative adversarial networks (GANs) have shown remarkable success in generating realistic data from some predefined prior distribution. In practice, semantic information might be represented by some latent distribution learned from data. We propose an LCCGAN model with local coordinate coding (LCC) to improve the performance of generating data.
  arXiv  Detail & Related papers  (2020-07-28T09:17:50Z)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)
• Semi-Supervised Learning with Normalizing Flows [54.376602201489995]
  FlowGMM is an end-to-end approach to generative semi supervised learning with normalizing flows. We show promising results on a wide range of applications, including AG-News and Yahoo Answers text data.
  arXiv  Detail & Related papers  (2019-12-30T17:36:33Z)

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.