  We introduce a new dataset called Synthetic COVID-19 Chest X-ray Dataset for
training machine learning models. The dataset consists of 21,295 synthetic
COVID-19 chest X-ray images to be used for computer-aided diagnosis. These
images, generated via an unsupervised domain adaptation approach, are of high
quality. We find that the synthetic images not only improve performance of
various deep learning architectures when used as additional training data under
heavy imbalance conditions, but also detect the target class with high
confidence. We also find that comparable performance can also be achieved when
trained only on synthetic images. Further, salient features of the synthetic
COVID-19 images indicate that the distribution is significantly different from
Non-COVID-19 classes, enabling a proper decision boundary. We hope the
availability of such high fidelity chest X-ray images of COVID-19 will
encourage advances in the development of diagnostic and/or management tools.
