Table2Image: Lightweight Tabular Learning with Generated Proxy Representations and Reliability Diagnostics

Deep tabular models should ideally balance predictive performance, parameter efficiency, and robustness to imperfect learning signals---properties that are rarely considered jointly. We present Table2Image, a lightweight tabular learning model built around a learned generation pathway that maps tabular inputs into intermediate, structured proxy representations. We additionally examine a variant with variance inflation factor (VIF)-informed initialization, which downweights highly collinear features at the start of training. Across datasets from OpenML-CC18 and TabZilla, Table2Image achieves competitive clean predictive performance while remaining compact relative to several large-scale neural baselines. We further introduce a unified, severity-controlled evaluation protocol under three imperfect learning conditions---irrelevant inputs, corrupted supervision, and unstable shortcut associations---that combines performance-based robustness measures with realization-level instability diagnostics for reliability characterization. Table2Image maintains a favorable balance of performance, robustness, and compactness. Controlled ablations indicate that the learned generation pathway is a key driver of the observed gains.