Synthetic Face Datasets Generation via Latent Space Exploration from Brownian Identity Diffusion

• URL: http://arxiv.org/abs/2405.00228v1
• Date: Tue, 30 Apr 2024 22:32:02 GMT
• Title: Synthetic Face Datasets Generation via Latent Space Exploration from Brownian Identity Diffusion
• Authors: David Geissbühler, Hatef Otroshi Shahreza, Sébastien Marcel,
• Abstract summary: Face Recognition (FR) models are trained on large-scale datasets, which have privacy and ethical concerns. Lately, the use of synthetic data to complement or replace genuine data for the training of FR models has been proposed. We introduce a new method, inspired by the physical motion of soft particles subjected to Brownian forces, allowing us to sample identities in a latent space under various constraints. With this in hands, we generate several face datasets and benchmark them by training FR models, showing that data generated with our method exceeds the performance of previously GAN-based datasets and achieves competitive performance with state-of-the-
• Score: 20.352548473293993
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Face Recognition (FR) models are trained on large-scale datasets, which have privacy and ethical concerns. Lately, the use of synthetic data to complement or replace genuine data for the training of FR models has been proposed. While promising results have been obtained, it still remains unclear if generative models can yield diverse enough data for such tasks. In this work, we introduce a new method, inspired by the physical motion of soft particles subjected to stochastic Brownian forces, allowing us to sample identities distributions in a latent space under various constraints. With this in hands, we generate several face datasets and benchmark them by training FR models, showing that data generated with our method exceeds the performance of previously GAN-based datasets and achieves competitive performance with state-of-the-art diffusion-based synthetic datasets. We also show that this method can be used to mitigate leakage from the generator's training set and explore the ability of generative models to generate data beyond it.

Related papers

• Enhancing Domain Diversity in Synthetic Data Face Recognition with Dataset Fusion [4.910937238451485]
  We propose a solution by combining two state-of-the-art synthetic face datasets generated using architecturally distinct backbones.<n>This fusion reduces model-specific artifacts, enhances diversity in pose, lighting, and demographics, and implicitly regularizes the face recognition model by emphasizing identity-relevant features.
  arXiv  Detail & Related papers  (2025-07-22T17:36:48Z)
• Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training [4.815212947276105]
  Programmatically generated synthetic data has been used in differential private training for classification to avoid privacy leakage.<n>The model trained with synthetic data generates unrealistic random images, raising challenges to adapt synthetic data for generative models.<n>We propose DPSynGen, which leverages generated synthetic data in diffusion models to address this challenge.
  arXiv  Detail & Related papers  (2024-12-13T04:22:23Z)
• Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data [104.30479583607918]
  2nd FRCSyn-onGoing challenge is based on the 2nd Face Recognition Challenge in the Era of Synthetic Data (FRCSyn), originally launched at CVPR 2024.<n>We focus on exploring the use of synthetic data both individually and in combination with real data to solve current challenges in face recognition.
  arXiv  Detail & Related papers  (2024-12-02T11:12:01Z)
• HyperFace: Generating Synthetic Face Recognition Datasets by Exploring Face Embedding Hypersphere [22.8742248559748]
  Face recognition datasets are often collected by crawling Internet and without individuals' consents, raising ethical and privacy concerns.<n> Generating synthetic datasets for training face recognition models has emerged as a promising alternative.<n>We propose a new synthetic dataset generation approach, called HyperFace.
  arXiv  Detail & Related papers  (2024-11-13T09:42:12Z)
• Unveiling Synthetic Faces: How Synthetic Datasets Can Expose Real Identities [22.8742248559748]
  We show that in 6 state-of-the-art synthetic face recognition datasets, several samples from the original real dataset are leaked. This paper is the first work which shows the leakage from training data of generator models into the generated synthetic face recognition datasets.
  arXiv  Detail & Related papers  (2024-10-31T15:17:14Z)
• 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)
• SDFR: Synthetic Data for Face Recognition Competition [51.9134406629509]
  Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. Recently several works proposed generating synthetic face recognition datasets to mitigate concerns in web-crawled face recognition datasets. This paper presents the summary of the Synthetic Data for Face Recognition (SDFR) Competition held in conjunction with the 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024) The SDFR competition was split into two tasks, allowing participants to train face recognition systems using new synthetic datasets and/or existing ones.
  arXiv  Detail & Related papers  (2024-04-06T10:30:31Z)
• Distribution-Aware Data Expansion with Diffusion Models [55.979857976023695]
  We propose DistDiff, a training-free data expansion framework based on the distribution-aware diffusion model. DistDiff consistently enhances accuracy across a diverse range of datasets compared to models trained solely on original data.
  arXiv  Detail & Related papers  (2024-03-11T14:07:53Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• Towards Theoretical Understandings of Self-Consuming Generative Models [56.84592466204185]
  This paper tackles the emerging challenge of training generative models within a self-consuming loop. We construct a theoretical framework to rigorously evaluate how this training procedure impacts the data distributions learned by future models. We present results for kernel density estimation, delivering nuanced insights such as the impact of mixed data training on error propagation.
  arXiv  Detail & Related papers  (2024-02-19T02:08:09Z)
• Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark [56.8042116967334]
  Synthetic data serves as an alternative in training machine learning models. ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task. This paper explores the potential of integrating data-centric AI techniques to guide the synthetic data generation process.
  arXiv  Detail & Related papers  (2023-10-25T20:32:02Z)
• Private Synthetic Data Meets Ensemble Learning [15.425653946755025]
  When machine learning models are trained on synthetic data and then deployed on real data, there is often a performance drop. We introduce a new ensemble strategy for training downstream models, with the goal of enhancing their performance when used on real data.
  arXiv  Detail & Related papers  (2023-10-15T04:24:42Z)
• On the Stability of Iterative Retraining of Generative Models on their own Data [56.153542044045224]
  We study the impact of training generative models on mixed datasets. We first prove the stability of iterative training under the condition that the initial generative models approximate the data distribution well enough. We empirically validate our theory on both synthetic and natural images by iteratively training normalizing flows and state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2023-09-30T16:41:04Z)
• 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)
• GANDiffFace: Controllable Generation of Synthetic Datasets for Face Recognition with Realistic Variations [2.7467281625529134]
  This study introduces GANDiffFace, a novel framework for the generation of synthetic datasets for face recognition. GANDiffFace combines the power of Generative Adversarial Networks (GANs) and Diffusion models to overcome the limitations of existing synthetic datasets.
  arXiv  Detail & Related papers  (2023-05-31T15:49:12Z)
• Phased Data Augmentation for Training a Likelihood-Based Generative Model with Limited Data [0.0]
  Generative models excel in creating realistic images, yet their dependency on extensive datasets for training presents significant challenges. Current data-efficient methods largely focus on GAN architectures, leaving a gap in training other types of generative models. "phased data augmentation" is a novel technique that addresses this gap by optimizing training in limited data scenarios without altering the inherent data distribution.
  arXiv  Detail & Related papers  (2023-05-22T03:38:59Z)
• Private Gradient Estimation is Useful for Generative Modeling [25.777591229903596]
  We present a new private generative modeling approach where samples are generated via Hamiltonian dynamics with gradients of the private dataset estimated by a well-trained network. Our model is able to generate data with a resolution of 256x256.
  arXiv  Detail & Related papers  (2023-05-18T02:51:17Z)
• Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets [83.749895930242]
  We propose two techniques for producing high-quality naturalistic synthetic occluded faces. We empirically show the effectiveness and robustness of both methods, even for unseen occlusions. We present two high-resolution real-world occluded face datasets with fine-grained annotations, RealOcc and RealOcc-Wild.
  arXiv  Detail & Related papers  (2022-05-12T17:03:57Z)

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.