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The exploration of few-shot tabular learning becomes imperative. Tabular datais a versatile representation that captures diverse information, yet it is notexempt from limitations, property of data and model size. Labeling extensivetabular data can be challenging, and it may not be feasible to capture everyimportant feature. Few-shot tabular learning, however, remains relativelyunexplored, primarily due to scarcity of shared information among independentdatasets and the inherent ambiguity in defining boundaries within tabular data.To the best of our knowledge, no meaningful and unrestricted few-shot tabularlearning techniques have been developed without imposing constraints on thedataset. In this paper, we propose an innovative framework called TablEye,which aims to overcome the limit of forming prior knowledge for tabular data byadopting domain transformation. It facilitates domain transformation bygenerating tabular images, which effectively conserve the intrinsic semanticsof the original tabular data. This approach harnesses rigorously testedfew-shot learning algorithms and embedding functions to acquire and apply priorknowledge. Leveraging shared data domains allows us to utilize this priorknowledge, originally learned from the image domain. Specifically, TablEyedemonstrated a superior performance by outstripping the TabLLM in a 4-shot taskwith a maximum 0.11 AUC and a STUNT in a 1- shot setting, where it led onaverage by 3.17% accuracy.

TablEye: Seeing small Tables through the Lens of Images