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The popularity of learning from data with machine learning and neuralnetworks has lead to the creation of many new datasets for almost every problemdomain. However, even within a single domain, these datasets are oftencollected with disparate features, sampled from different sub-populations, andrecorded at different time points. Even with the plethora of individualdatasets, large data science projects can be difficult as it is often nottrivial to merge these smaller datasets. Inherent challenges in some domainssuch as medicine also makes it very difficult to create large single sourcedatasets or multi-source datasets with identical features. Instead of trying tomerge these non-matching datasets directly, we propose a neural networkarchitecture that can provide data augmentation using features common betweenthese datasets. Our results show that this style of data augmentation can workfor both image and tabular data.

Neural Network Architecture for Database Augmentation Using Shared Features