RDPD: Rich Data Helps Poor Data via Imitation

TL;DR

This paper introduces RDPD, a knowledge distillation method that leverages rich, multi-modal data from high-quality environments to improve models trained on poor, uni-modal data in related settings.

Contribution

The paper presents a novel KD approach that transfers knowledge from rich-data models to enhance poor-data models without sharing raw multi-modal data.

Findings

Distilled models outperform baselines across datasets.

Achieves up to 24.56% improvement in PR-AUC.

Outperforms state-of-the-art KD models by significant margins.

Abstract

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.