  In many situations, we need to build and deploy separate models in related
environments with different data qualities. For example, an environment with
strong observation equipments (e.g., intensive care units) often provides
high-quality multi-modal data, which are acquired from multiple sensory devices
and have rich-feature representations. On the other hand, an environment with
poor observation equipment (e.g., at home) only provides low-quality, uni-modal
data with poor-feature representations. To deploy a competitive model in a
poor-data environment without requiring direct access to multi-modal data
acquired from a rich-data environment, this paper develops and presents a
knowledge distillation (KD) method (RDPD) to enhance a predictive model trained
on poor data using knowledge distilled from a high-complexity model trained on
rich, private data. We evaluated RDPD on three real-world datasets and shown
that its distilled model consistently outperformed all baselines across all
datasets, especially achieving the greatest performance improvement over a
model trained only on low-quality data by 24.56% on PR-AUC and 12.21% on
ROC-AUC, and over that of a state-of-the-art KD model by 5.91% on PR-AUC and
4.44% on ROC-AUC.
