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cs.CV2026
VG-TIE: An interpretable tabular-to-image encoding method based on visibility graphs
David Chushig-Muzo, Luis M. López-Ramos, Ángeles Rodríguez de Cara +3
Tabular-to-image encoding methods enable the application of models based on both convolutional neural networks and vision transformers to tabular data, transforming feature vectors…
cs.CV2026
TabSOM: A tabular-to-image encoding method based on self-organizing maps
David Chushig-Muzo, María Ángeles Rodríguez de Cara, Eva Milara +3
Tabular-to-image methods have emerged as novel approaches to leverage the high predictive performance of convolutional neural networks and vision transformers. They convert tabular…
cs.CV2026
Test-Time Augmentation for Tabular-to-Image Classifiers under Distribution Shifts
Malena Loza, Felipe Grijalva, Eva Milara +3
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…