Generative Foundation Models (GFMs) have produced synthetic data with
remarkable quality in modalities such as images and text. However, applying
GFMs to tabular data poses significant challenges due to the inherent
heterogeneity of table features. Existing cross-table learning frameworks are
hindered by the absence of both a generative model backbone and a decoding
mechanism for heterogeneous feature values. To overcome these limitations, we
introduce the Cross-Table Synthesizer (CTSyn), a diffusion-based foundational
model tailored for tabular data generation. CTSyn introduces three major
components: an aggregator that consolidates heterogeneous tables into a unified
latent space; a conditional latent diffusion model for sampling from this
space; and type-specific decoders that reconstruct values of varied data types
from sampled latent vectors. Extensive testing on real-world datasets reveals
that CTSyn not only significantly outperforms existing table synthesizers in
utility and diversity, but also uniquely enhances performances of downstream
machine learning beyond what is achievable with real data, thus establishing a
new paradigm for synthetic data generation.

介绍了一种针对表格数据生成的基于扩散的跨表合成器（CTSyn），通过整合异构表格数据、条件潜在扩散模型和类型特定解码器，从采样的潜在向量中重构不同数据类型的值，提高了合成功能和多样性，同时在下游机器学习任务中表现出对真实数据无法达到的性能提升，从而为合成数据生成建立了新的范式。