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Universidad San Francisco de Quito (USFQ)
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TTA can dramatically boost the robustness of tabular-to-image classifiers under distribution shifts, with certain strategies outperforming others by a significant margin.
All evaluated Tabular Foundation Models suffer significant performance drops under distribution shifts, revealing a critical vulnerability in their robustness.