Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions

TL;DR

This survey reviews recent advances in deep learning for tabular data, highlighting challenges, novel architectures, and future research directions to improve representation learning across diverse applications.

Contribution

It provides a holistic overview of recent techniques, emphasizing the integration of data augmentation, neural architectures, and learning objectives, including self-supervised and transformer-based models.

Findings

Recent methods improve representation quality for tabular data.

Self-supervised learning is increasingly influential.

Transformer models are adapting to tabular data challenges.

Abstract

Tabular data remains one of the most prevalent data types across a wide range of real-world applications, yet effective representation learning for this domain poses unique challenges due to its irregular patterns, heterogeneous feature distributions, and complex inter-column dependencies. This survey provides a comprehensive review of state-of-the-art techniques in tabular data representation learning, structured around three foundational design elements: training data, neural architectures, and learning objectives. Unlike prior surveys that focus primarily on either architecture design or learning strategies, we adopt a holistic perspective that emphasizes the universality and robustness of representation learning methods across diverse downstream tasks. We examine recent advances in data augmentation and generation, specialized neural network architectures tailored to tabular data, and innovative learning objectives that enhance representation quality. Additionally, we highlight the growing influence of self-supervised learning and the adaptation of transformer-based foundation models for tabular data. Our review is based on a systematic literature search using rigorous inclusion criteria, encompassing 127 papers published since 2020 in top-tier conferences and journals. Through detailed analysis and comparison, we identify emerging trends, critical gaps, and promising directions for future research, aiming to guide the development of more generalizable and effective tabular data representation methods.