Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios

While synthetic tabular data generation using Deep Generative Models (DGMs) offers a compelling solution to data scarcity and privacy concerns, their effectiveness relies on substantial training data, often unavailable in real-world applications. This paper addresses this challenge by proposing a novel methodology for generating realistic and reliable synthetic tabular data with DGMs in limited real-data environments. Our approach proposes several ways to generate an artificial inductive bias in a DGM through transfer learning and meta-learning techniques. We explore and compare four different methods within this framework, demonstrating that transfer learning strategies like pre-training and model averaging outperform meta-learning approaches, like Model-Agnostic Meta-Learning, and Domain Randomized Search. We validate our approach using two state-of-the-art DGMs, namely, a Variational Autoencoder and a Generative Adversarial Network, to show that our artificial inductive bias fuels superior synthetic data quality, as measured by Jensen-Shannon divergence, achieving relative gains of up to 50\% when using our proposed approach. This methodology has broad applicability in various DGMs and machine learning tasks, particularly in areas like healthcare and finance, where data scarcity is often a critical issue.