A comprehensive benchmark of spatial encoding methods for tabular data with deep neural networks

• Benchmarked tabular data into synthetic image methods for regression and classification on diverse datasets. • Analyzed computational trade-offs and scalability, guiding applicability to large-scale tabular data. • Showed hybrid models (CNN+MLP, ViT+MLP) consistently improve performance with synthetic images. • Performed critical analysis of key limitations in current tabular data into synthetic image methods. Despite the success of deep neural networks on perceptual data, their performance on tabular data remains limited, where traditional models still outperform them. A promising alternative is to transform tabular data into synthetic images, enabling the use of vision architectures such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). However, the literature lacks a large-scale, standardized benchmark evaluating these transformation techniques. This work presents the first comprehensive evaluation of nine spatial encoding methods across 24 diverse regression and classification datasets. We assess performance, scalability, and computational trade-offs under a unified framework with rigorous hyperparameter optimization. Our results reveal a performance landscape structured by data regimes, defined by sample size ( N ) and dimensionality ( d ), and show that the transformation method exerts a significantly stronger influence on predictive performance than the chosen vision architecture. In particular, REFINED emerges as the most robust transformation across tasks and datasets. Hybrid models (CNN+MLP, ViT+MLP) consistently reduce predictive variance, offering advantages especially in smaller datasets, yet play a secondary role. These findings suggest that transforming tabular data into synthetic images is a powerful, yet data-dependent, strategy. This benchmark provides clear guidance for researchers and practitioners, offering key insights into scalability, transformation behavior, and architectural interplay, establishing a comprehensive reference for future research on spatial encodings for tabular data.