Controlling the Fidelity and Diversity of Deep Generative Models via Pseudo Density

• URL: http://arxiv.org/abs/2407.08659v2
• Date: Thu, 3 Oct 2024 22:25:54 GMT
• Title: Controlling the Fidelity and Diversity of Deep Generative Models via Pseudo Density
• Authors: Shuangqi Li, Chen Liu, Tong Zhang, Hieu Le, Sabine Süsstrunk, Mathieu Salzmann,
• Abstract summary: We introduce an approach to bias deep generative models, such as GANs and diffusion models, towards generating data with enhanced fidelity or increased diversity. Our approach involves manipulating the distribution of training and generated data through a novel metric for individual samples, named pseudo density.
• Score: 70.14884528360199
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce an approach to bias deep generative models, such as GANs and diffusion models, towards generating data with either enhanced fidelity or increased diversity. Our approach involves manipulating the distribution of training and generated data through a novel metric for individual samples, named pseudo density, which is based on the nearest-neighbor information from real samples. Our approach offers three distinct techniques to adjust the fidelity and diversity of deep generative models: 1) Per-sample perturbation, enabling precise adjustments for individual samples towards either more common or more unique characteristics; 2) Importance sampling during model inference to enhance either fidelity or diversity in the generated data; 3) Fine-tuning with importance sampling, which guides the generative model to learn an adjusted distribution, thus controlling fidelity and diversity. Furthermore, our fine-tuning method demonstrates the ability to improve the Frechet Inception Distance (FID) for pre-trained generative models with minimal iterations.

Related papers

• GUD: Generation with Unified Diffusion [40.64742332352373]
  Diffusion generative models transform noise into data by inverting a process that progressively adds noise to data samples. We develop a unified framework for diffusion generative models with greatly enhanced design freedom.
  arXiv  Detail & Related papers  (2024-10-03T16:51:14Z)
• Constrained Diffusion Models via Dual Training [80.03953599062365]
  Diffusion processes are prone to generating samples that reflect biases in a training dataset. We develop constrained diffusion models by imposing diffusion constraints based on desired distributions. We show that our constrained diffusion models generate new data from a mixture data distribution that achieves the optimal trade-off among objective and constraints.
  arXiv  Detail & Related papers  (2024-08-27T14:25:42Z)
• Provable Statistical Rates for Consistency Diffusion Models [87.28777947976573]
  Despite the state-of-the-art performance, diffusion models are known for their slow sample generation due to the extensive number of steps involved. This paper contributes towards the first statistical theory for consistency models, formulating their training as a distribution discrepancy minimization problem.
  arXiv  Detail & Related papers  (2024-06-23T20:34:18Z)
• Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI [29.13807697733638]
  We build on the remarkable achievements in generative sampling of natural images. We propose an innovative challenge, potentially overly ambitious, which involves generating samples that resemble images. The statistical challenge lies in the small sample size, sometimes consisting of a few hundred subjects.
  arXiv  Detail & Related papers  (2024-04-10T22:35:06Z)
• Improving Out-of-Distribution Robustness of Classifiers via Generative Interpolation [56.620403243640396]
  Deep neural networks achieve superior performance for learning from independent and identically distributed (i.i.d.) data. However, their performance deteriorates significantly when handling out-of-distribution (OoD) data. We develop a simple yet effective method called Generative Interpolation to fuse generative models trained from multiple domains for synthesizing diverse OoD samples.
  arXiv  Detail & Related papers  (2023-07-23T03:53:53Z)
• Phoenix: A Federated Generative Diffusion Model [6.09170287691728]
  Training generative models on large centralized datasets can pose challenges in terms of data privacy, security, and accessibility. This paper proposes a novel method for training a Denoising Diffusion Probabilistic Model (DDPM) across multiple data sources using Federated Learning (FL) techniques.
  arXiv  Detail & Related papers  (2023-06-07T01:43:09Z)
• Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models [77.83923746319498]
  We propose a framework called Diff-Instruct to instruct the training of arbitrary generative models. We show that Diff-Instruct results in state-of-the-art single-step diffusion-based models. Experiments on refining GAN models show that the Diff-Instruct can consistently improve the pre-trained generators of GAN models.
  arXiv  Detail & Related papers  (2023-05-29T04:22:57Z)
• How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models [95.8037674226622]
  We introduce a 3-dimensional evaluation metric that characterizes the fidelity, diversity and generalization performance of any generative model in a domain-agnostic fashion. Our metric unifies statistical divergence measures with precision-recall analysis, enabling sample- and distribution-level diagnoses of model fidelity and diversity.
  arXiv  Detail & Related papers  (2021-02-17T18:25:30Z)

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.