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Updated: Aug 4, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Deep Neural Networks and Tabular Data: A Survey.

Vadim Borisov, Tobias Leemann, Kathrin Sebler

IEEE Transactions on Neural Networks and Learning Systems

|April 4, 2023

Summary

Deep neural networks struggle with heterogeneous tabular data, despite their success on homogeneous datasets. Gradient-boosted tree ensembles still outperform deep learning models in supervised learning tasks on tabular data.

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Area of Science:

Computer Science
Machine Learning
Artificial Intelligence

Background:

Tabular data is ubiquitous and crucial for many applications.
Deep neural networks excel on homogeneous data but face challenges with heterogeneous tabular data.
Existing research on deep learning for tabular data is fragmented.

Purpose of the Study:

To provide a comprehensive overview of state-of-the-art deep learning methods for tabular data.
To categorize and discuss deep learning approaches for tabular data inference, generation, and explanation.
To empirically compare deep learning methods against traditional machine learning algorithms on real-world tabular datasets.

Main Methods:

Categorization of deep learning methods into data transformations, specialized architectures, and regularization models.
Review of deep learning techniques for tabular data generation and model explanation.
Empirical comparison of 11 deep learning approaches with traditional machine learning methods on five diverse tabular datasets.

Main Results:

Gradient-boosted tree ensembles generally outperform deep learning models on supervised learning tasks with tabular data.
The study highlights challenges and open research questions in deep learning for tabular data.
Publicly available benchmarks are provided for future research.

Conclusions:

Deep learning research for tabular data may be stagnating.
Traditional methods like gradient-boosted trees remain highly competitive for tabular data.
This work serves as a foundational resource for researchers and practitioners in deep learning for tabular data.