Despite the prevalence of tabular datasets, few-shot learning remains under-explored within this domain. Existing few-shot methods are not directly applicable to tabular datasets due to varying column relationships, meanings, and permutational invariance. To address these challenges, we propose FLAT-a novel approach to tabular few-shot learning, encompassing knowledge sharing between datasets with heterogeneous feature spaces. Utilizing an encoder inspired by Dataset2Vec, FLAT learns low-dimensional embeddings of datasets and their individual columns, which facilitate knowledge transfer and generalization to previously unseen datasets. A decoder network parametrizes the predictive target network, implemented as a Graph Attention Network, to accommodate the heterogeneous nature of tabular datasets. Experiments on a diverse collection of 118 UCI datasets demonstrate FLAT's successful generalization to new tabular datasets and a considerable improvement over the baselines.

翻译：尽管表格数据集普遍存在，少样本学习在该领域中仍未得到充分探索。现有的少样本方法由于列关系、含义的不同以及排列不变性，无法直接应用于表格数据集。为应对这些挑战，我们提出了FLAT——一种新颖的表格少样本学习方法，涵盖具有异质特征空间的数据集间的知识共享。FLAT利用受Dataset2Vec启发的编码器，学习数据集及其各列的低维嵌入，从而促进知识迁移和对未见数据集的泛化。解码器网络对以图注意力网络实现的预测目标网络进行参数化，以适应表格数据集的异质特性。在118个UCI数据集的多样化集合上进行的实验表明，FLAT成功泛化到新的表格数据集，并相较于基线方法取得了显著提升。