Recent advances in tabular data generation have greatly enhanced synthetic data quality. However, extending diffusion models to tabular data is challenging due to the intricately varied distributions and a blend of data types of tabular data. This paper introduces Tabsyn, a methodology that synthesizes tabular data by leveraging a diffusion model within a variational autoencoder (VAE) crafted latent space. The key advantages of the proposed Tabsyn include (1) Generality: the ability to handle a broad spectrum of data types by converting them into a single unified space and explicitly capture inter-column relations; (2) Quality: optimizing the distribution of latent embeddings to enhance the subsequent training of diffusion models, which helps generate high-quality synthetic data, (3) Speed: much fewer number of reverse steps and faster synthesis speed than existing diffusion-based methods. Extensive experiments on six datasets with five metrics demonstrate that Tabsyn outperforms existing methods. Specifically, it reduces the error rates by 86% and 67% for column-wise distribution and pair-wise column correlation estimations compared with the most competitive baselines.
Tabular data synthesis is an emerging approach to circumvent strict regulations on data privacy while discovering knowledge through big data. Although state-of-the-art AI-based tabular data synthesizers, e.g., table-GAN, CTGAN, TVAE, and CTAB-GAN, are effective at generating synthetic tabular data, their training is sensitive to column permutations of input data. In this paper, we first conduct an extensive empirical study to disclose such a property of permutation invariance and an in-depth analysis of the existing synthesizers. We show that changing the input column order worsens the statist…
Tabular data synthesis has received wide attention in the literature. This is because available data is often limited, incomplete, or cannot be obtained easily, and data privacy is becoming increasingly important. In this work, we present a generalized GAN framework for tabular synthesis, which combines the adversarial training of GANs and the negative log-density regularization of invertible neural networks. The proposed framework can be used for two distinctive objectives. First, we can further improve the synthesis quality, by decreasing the negative log-density of real records in the proce…
While data sharing is crucial for knowledge development, privacy concerns and strict regulation (e.g., European General Data Protection Regulation (GDPR)) limit its full effectiveness. Synthetic tabular data emerges as alternative to enable data sharing while fulfilling regulatory and privacy constraints. State-of-the-art tabular data synthesizers draw methodologies from Generative Adversarial Networks (GAN). As GANs improve the synthesized data increasingly resemble the real data risking to leak privacy. Differential privacy (DP) provides theoretical guarantees on privacy loss but degrades da…
Bayesian Generative Adversarial Networks via Gaussian Approximation for Tabular Data Synthesis
Department of Social Statistics, The University of Manchester（社会统计系，曼彻斯特大学）；Alliance Manchester Business School, The University of Manchester（曼彻斯特商业联盟学院，曼彻斯特大学）
QTabGAN: A Hybrid Quantum-Classical GAN for Tabular Data Synthesis
Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, India（计算机科学与工程系，印度理工学院（BHU），瓦拉纳西，印度）；Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom（应用数学与理论物理系，剑桥大学，剑桥，英国）
Synthetic tabular data emerges as an alternative for sharing knowledge while adhering to restrictive data access regulations, e.g., European General Data Protection Regulation (GDPR). Mainstream state-of-the-art tabular data synthesizers draw methodologies from Generative Adversarial Networks (GANs), which are composed of a generator and a discriminator. While convolution neural networks are shown to be a better architecture than fully connected networks for tabular data synthesizing, two key properties of tabular data are overlooked: (i) the global correlation across columns, and (ii) invaria…
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Conditionality has become a core component for Generative Adversarial Networks (GANs) for generating synthetic images. GANs are usually using latent conditionality to control the generation process. However, tabular data only contains manifest variables. Thus, latent conditionality either restricts the generated data or does not produce sufficiently good results. Therefore, we propose a new methodology to include conditionality in tabular GANs inspired by image completion methods. This article presents ciDATGAN, an evolution of the Directed Acyclic Tabular GAN (DATGAN) that has already been sh…
Agent-based transportation modelling has become the standard to simulate travel behaviour, mobility choices and activity preferences using disaggregate travel demand data for entire populations, data that are not typically readily available. Various methods have been proposed to synthesize population data for this purpose. We present a Composite Travel Generative Adversarial Network (CTGAN), a novel deep generative model to estimate the underlying joint distribution of a population, that is capable of reconstructing composite synthetic agents having tabular (e.g. age and sex) as well as sequen…
DP-TLDM: Differentially Private Tabular Latent Diffusion Model
TU Delft, Delft, Netherlands , University of Neuchâtel, Neuchâtel, Switzerland Universidad de los Andes, Bogotá, Colombia Centrum Wiskunde \& Informatica, Amsterdam, Netherlands
Synthesizing tabular data is attracting much attention these days for various purposes. With sophisticate synthetic data, for instance, one can augment its training data. For the past couple of years, tabular data synthesis techniques have been greatly improved. Recent work made progress to address many problems in synthesizing tabular data, such as the imbalanced distribution and multimodality problems. However, the data utility of state-of-the-art methods is not satisfactory yet. In this work, we significantly improve the utility by designing our generator and discriminator based on neural o…
Multimodal synthesis of MRI and tabular data with diffusion in a joint latent space via cross-attention
Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所MEVIS）；Leibniz Institute for Prevention Research and Epidemiology – BIPS（莱比锡预防研究与流行病学研究所 – BIPS）；Faculty of Mathematics and Computer Science, University of Bremen（不莱梅大学数学与计算机科学学院）；Faculty of Physics and Electrical Engineering, University of Bremen（不莱梅大学物理与电气工程学院）
Structural Equation-VAE: Disentangled Latent Representations for Tabular Data
Department of Politics and Public Administration（政治与公共行政系）；The University of Hong Kong（香港大学）；School of Computer Science（计算机科学系）；Carnegie Mellon University（卡内基梅隆大学）；Department of Applied Social Sciences（应用社会科学系）；The Hong Kong Polytechnic University（香港理工大学）
TabularQGAN: A quantum generative model for tabular data synthesis
SAP SE（SAP公司）；BASF Digital Solutions（巴斯夫数字解决方案）；QUTAC Quantum Technology and Application Consortium（QUTAC量子技术与应用联盟）；Technical University of Munich CIT（慕尼黑技术大学CIT）；Ludwig-Maximilians-Universität München（慕尼黑路德维希-马克西米利安大学）；Merck KGaA（默克公司）
Department of Social Statistics（社会统计系）；The University of Manchester（曼彻斯特大学）；Alliance Manchester Business School（曼彻斯特商业联盟学院）；Amsterdam Machine Learning Lab（阿姆斯特丹机器学习实验室）；University of Amsterdam（阿姆斯特丹大学）
CTSyn: A Foundation Model for Cross Tabular Data Generation
Department of Statistics and Data Science, University of California Los Angeles（统计与数据科学系，加州大学洛杉矶分校）；Department of Computer Science, University of California Los Angeles（计算机科学系，加州大学洛杉矶分校）
Diff-DDoS: Realistic Cyber-Physical Attack Synthesis and Robust Detection for 5G-Enabled CPS Using Tabular Diffusion Models
The Hong Kong Polytechnic University（香港理工大学）；Xi’an Jiaotong University（西安交通大学）；The Hang Seng University of Hong Kong（香港恒生大学）；Hong Kong Shue Yan University（香港树仁大学）
École de Technologie Supérieure (ÉTS), Université du Québec（魁北克大学高等技术学院）；Fédération des Caisses Desjardins du Québec（魁北克德信联合会）；University of Windsor（温莎大学）
This paper introduces a novel generative adversarial network (GAN) for synthesizing large-scale tabular databases which contain various features such as continuous, discrete, and binary. Technically, our GAN belongs to the category of class-conditioned generative models with a predefined conditional vector. However, we propose a new formulation for deriving such a vector incorporating both binary and discrete features simultaneously. We refer to this noble definition as compound conditional vector and employ it for training the generator network. The core architecture of this network is a thre…
TabGift: A Context-Efficient Foundation Model for Tabular Data Generation
School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）
University of California, Los Angeles（加州大学洛杉矶分校）；Stevens Institute of Technology（斯蒂文斯理工学院）
This paper proposes a latent space energy-based prior model for semi-supervised learning. The model stands on a generator network that maps a latent vector to the observed example. The energy term of the prior model couples the latent vector and a symbolic one-hot vector, so that classification can be based on the latent vector inferred from the observed example. In our learning method, the symbol-vector coupling, the generator network and the inference network are learned jointly. Our method is applicable to semi-supervised learning in various data domains such as image, text, and tabular dat…
TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data
Simon Fraser University（西蒙弗雷泽大学）；Zurich University of the Arts（苏黎世艺术大学）
We present the Latent Timbre Synthesis (LTS), a new audio synthesis method using Deep Learning. The synthesis method allows composers and sound designers to interpolate and extrapolate between the timbre of multiple sounds using the latent space of audio frames. We provide the details of two Variational Autoencoder architectures for LTS, and compare their advantages and drawbacks. The implementation includes a fully working application with graphical user interface, called \textit{interpolate\_two}, which enables practitioners to explore the timbre between two audio excerpts of their selection…
University of Copenhagen（哥本哈根大学）；Rigshospitalet（哥本哈根大学国立医院）
Medical imaging plays a vital role in modern diagnostics and treatment. The temporal nature of disease or treatment progression often results in longitudinal data. Due to the cost and potential harm, acquiring large medical datasets necessary for deep learning can be difficult. Medical image synthesis could help mitigate this problem. However, until now, the availability of GANs capable of synthesizing longitudinal volumetric data has been limited. To address this, we use the recent advances in latent space-based image editing to propose a novel joint learning scheme to explicitly embed tempor…
Nagoya Institute of Technology（名古屋工业大学）；Microsoft Development Co., Ltd.（微软开发有限公司）
This paper proposes a hierarchical generative model with a multi-grained latent variable to synthesize expressive speech. In recent years, fine-grained latent variables are introduced into the text-to-speech synthesis that enable the fine control of the prosody and speaking styles of synthesized speech. However, the naturalness of speech degrades when these latent variables are obtained by sampling from the standard Gaussian prior. To solve this problem, we propose a novel framework for modeling the fine-grained latent variables, considering the dependence on an input text, a hierarchical ling…
LMU Munich（慕尼黑大学）；University Hospital, LMU Munich（慕尼黑大学附属医院）；Munich Center for Machine Learning（慕尼黑机器学习中心）
While recent advances in large-scale foundational models show promising results, their application to the medical domain has not yet been explored in detail. In this paper, we progress into the realms of large-scale modeling in medical synthesis by proposing Cheff - a foundational cascaded latent diffusion model, which generates highly-realistic chest radiographs providing state-of-the-art quality on a 1-megapixel scale. We further propose MaCheX, which is a unified interface for public chest datasets and forms the largest open collection of chest X-rays up to date. With Cheff conditioned on r…
We present fast, realistic image generation on high-resolution, multimodal datasets using hierarchical variational autoencoders (VAEs) trained on a deterministic autoencoder's latent space. In this two-stage setup, the autoencoder compresses the image into its semantic features, which are then modeled with a deep VAE. With this method, the VAE avoids modeling the fine-grained details that constitute the majority of the image's code length, allowing it to focus on learning its structural components. We demonstrate the effectiveness of our two-stage approach, achieving a FID of 9.34 on the Image…
Creation begins with understanding: LLMs as strategy designers for privacy-preserving tabular data synthesis
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Broad College of Business, Michigan State University（密歇根州立大学broad商学院）；Commonwealth Scientific and Industrial Research Organisation (CSIRO)（英联邦科学与工业研究组织）；Center of Excellence for Generative AI, King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学生成式人工智能卓越中心）；The Swiss AI Lab, IDSIA-USI/SUPSI（瑞士人工智能实验室IDSIA-USI/SUPSI）
Institut de Recherche et Coordination Acoustique Musique (IRCAM)（声学与音乐研究和协调研究所（IRCAM））；UPMC（巴黎第六大学）；CNRS（法国国家科学研究中心）
Recent studies show the ability of unsupervised models to learn invertible audio representations using Auto-Encoders. They enable high-quality sound synthesis but a limited control since the latent spaces do not disentangle timbre properties. The emergence of disentangled representations was studied in Variational Auto-Encoders (VAEs), and has been applied to audio. Using an additional perceptual regularization can align such latent representation with the previously established multi-dimensional timbre spaces, while allowing continuous inference and synthesis. Alternatively, some specific sou…
