Test-Time Augmentation for Tabular-to-Image Classifiers under Distribution Shifts

2026-08-04

AI Summary

This study addresses the limited out-of-distribution (OOD) generalization of tabular data after image-based encoding under distribution shift. It presents the first systematic evaluation of test-time augmentation (TTA) across multiple tabular-to-image transformation methods, including TINTO and DeepInsight. Leveraging the TableShift benchmark, the authors combine six encoding strategies with 25 TTA techniques spanning geometric, photometric, structural, frequency-domain, Mixup, and composite categories. Their analysis reveals that composite and photometric augmentations consistently enhance OOD performance, whereas frequency-domain transformations generally degrade it. This work demonstrates the robustness potential of TTA in tabular image classification and provides practical, low-variance augmentation protocols for real-world deployment.

Abstract

Tabular-to-image methods that convert tabular data into visual representations have emerged as a novel paradigm for leveraging the high performance of deep learning models. Despite their advantages, the robustness of these methods under distribution shifts remains under explored. Test-Time Augmentation (TTA) is an effective approach in image classification to improve model generalization and robustness, where predictions over multiple transformed views of each input are aggregated. This work evaluates the impact of TTA techniques on predictive performance under Out-Of-Distribution (OOD) for representations generated by tabular-to-image methods. Six tabular-to-image encoding methods were considered: TINTO, IGTD, DeepInsight, BIE, DistanceMatrix, Fotomics. Twenty-five TTA techniques were used, organized into six types: Geometric, Photometric, Structural, Frequency/Encoding, Mixup, and Composite. We employed two datasets from the TableShift benchmark (HELOC and Voting) that provide in-distribution and OOD test subsets designed to evaluate the effect of distribution shifts on tabular data. The results indicate that TTA improves OOD performance, with composite and photometric strategies providing the best trade-off between robustness and variance. In contrast, frequency-domain transformations that alter the encoder's feature-to-intensity mapping consistently degrade performance. These findings highlight TTA as a promising approach for improving the robustness and generalization of classifiers trained on image representations derived from tabular data, particularly under distribution shifts.

Problem

• Test-Time Augmentation
• Tabular-to-Image
• Distribution Shifts
• Out-Of-Distribution
• Robustness

Innovation

• Test-Time Augmentation
• Tabular-to-Image
• Distribution Shift
• Out-Of-Distribution Robustness
• Composite TTA