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TabSOM not only outperforms existing tabular-to-image methods but also enhances interpretability by revealing feature relationships and interactions.
TTA can dramatically boost the robustness of tabular-to-image classifiers under distribution shifts, with certain strategies outperforming others by a significant margin.
