  COVID-19 classification using chest Computed Tomography (CT) has been found
pragmatically useful by several studies. Due to the lack of annotated samples,
these studies recommend transfer learning and explore the choices of
pre-trained models and data augmentation. However, it is still unknown if there
are better strategies than vanilla transfer learning for more accurate COVID-19
classification with limited CT data. This paper provides an affirmative answer,
devising a novel `model' augmentation technique that allows a considerable
performance boost to transfer learning for the task. Our method systematically
reduces the distributional shift between the source and target domains and
considers augmenting deep learning with complementary representation learning
techniques. We establish the efficacy of our method with publicly available
datasets and models, along with identifying contrasting observations in the
previous studies.
