Gaussian Harmony: Attaining Fairness in Diffusion-based Face Generation Models

• URL: http://arxiv.org/abs/2312.14976v1
• Date: Thu, 21 Dec 2023 20:06:15 GMT
• Title: Gaussian Harmony: Attaining Fairness in Diffusion-based Face Generation Models
• Authors: Basudha Pal, Arunkumar Kannan, Ram Prabhakar Kathirvel, Alice J. O'Toole, Rama Chellappa
• Abstract summary: Diffusion models amplify the bias in the generation process, leading to an imbalance in distribution of sensitive attributes such as age, gender and race. We mitigate the bias by localizing the means of the facial attributes in the latent space of the diffusion model using Gaussian mixture models (GMM) Our results demonstrate that our approach leads to a more fair data generation in terms of representational fairness while preserving the quality of generated samples.
• Score: 31.688873613213392
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models have achieved great progress in face generation. However, these models amplify the bias in the generation process, leading to an imbalance in distribution of sensitive attributes such as age, gender and race. This paper proposes a novel solution to this problem by balancing the facial attributes of the generated images. We mitigate the bias by localizing the means of the facial attributes in the latent space of the diffusion model using Gaussian mixture models (GMM). Our motivation for choosing GMMs over other clustering frameworks comes from the flexible latent structure of diffusion model. Since each sampling step in diffusion models follows a Gaussian distribution, we show that fitting a GMM model helps us to localize the subspace responsible for generating a specific attribute. Furthermore, our method does not require retraining, we instead localize the subspace on-the-fly and mitigate the bias for generating a fair dataset. We evaluate our approach on multiple face attribute datasets to demonstrate the effectiveness of our approach. Our results demonstrate that our approach leads to a more fair data generation in terms of representational fairness while preserving the quality of generated samples.

Related papers

• Edge-preserving noise for diffusion models [4.435514696080208]
  We present a novel edge-preserving diffusion model that is a generalization of denoising diffusion probablistic models (DDPM) In particular, we introduce an edge-aware noise scheduler that varies between edge-preserving and isotropic Gaussian noise. We show that our model's generative process converges faster to results that more closely match the target distribution.
  arXiv  Detail & Related papers  (2024-10-02T13:29:52Z)
• Constrained Diffusion Models via Dual Training [80.03953599062365]
  We develop constrained diffusion models based on desired distributions informed by requirements. 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)
• Balancing Act: Distribution-Guided Debiasing in Diffusion Models [31.38505986239798]
  Diffusion Models (DMs) have emerged as powerful generative models with unprecedented image generation capability. DMs reflect the biases present in the training datasets. We present a method for debiasing DMs without relying on additional data or model retraining.
  arXiv  Detail & Related papers  (2024-02-28T09:53:17Z)
• Fair GANs through model rebalancing for extremely imbalanced class distributions [5.463417677777276]
  We present an approach to construct an unbiased generative adversarial network (GAN) from an existing biased GAN. We show results for the StyleGAN2 models while training on the Flickr Faces High Quality (FFHQ) dataset for racial fairness. We further validate our approach by applying it to an imbalanced CIFAR10 dataset which is also twice as large.
  arXiv  Detail & Related papers  (2023-08-16T19:20:06Z)
• Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
  Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance. We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T12:18:20Z)
• Analyzing Bias in Diffusion-based Face Generation Models [75.80072686374564]
  Diffusion models are increasingly popular in synthetic data generation and image editing applications. We investigate the presence of bias in diffusion-based face generation models with respect to attributes such as gender, race, and age. We examine how dataset size affects the attribute composition and perceptual quality of both diffusion and Generative Adversarial Network (GAN) based face generation models.
  arXiv  Detail & Related papers  (2023-05-10T18:22:31Z)
• Class-Balancing Diffusion Models [57.38599989220613]
  Class-Balancing Diffusion Models (CBDM) are trained with a distribution adjustment regularizer as a solution. Our method benchmarked the generation results on CIFAR100/CIFAR100LT dataset and shows outstanding performance on the downstream recognition task.
  arXiv  Detail & Related papers  (2023-04-30T20:00:14Z)
• Diffusing Gaussian Mixtures for Generating Categorical Data [21.43283907118157]
  We propose a generative model for categorical data based on diffusion models with a focus on high-quality sample generation. Our method of evaluation highlights the capabilities and limitations of different generative models for generating categorical data.
  arXiv  Detail & Related papers  (2023-03-08T14:55:32Z)
• Riemannian Score-Based Generative Modeling [56.20669989459281]
  We introduce score-based generative models (SGMs) demonstrating remarkable empirical performance. Current SGMs make the underlying assumption that the data is supported on a Euclidean manifold with flat geometry. This prevents the use of these models for applications in robotics, geoscience or protein modeling.
  arXiv  Detail & Related papers  (2022-02-06T11:57:39Z)
• Improving the Fairness of Deep Generative Models without Retraining [41.6580482370894]
  Generative Adversarial Networks (GANs) advance face synthesis through learning the underlying distribution of observed data. Despite the high-quality generated faces, some minority groups can be rarely generated from the trained models due to a biased image generation process. We propose an interpretable baseline method to balance the output facial attributes without retraining.
  arXiv  Detail & Related papers  (2020-12-09T03:20:41Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)

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.