Acquisition Policy

Acquisition policy research focuses on optimizing the process of data collection to improve efficiency and performance in various applications. Current efforts concentrate on developing adaptive acquisition strategies using techniques like reinforcement learning and conditional mutual information maximization, often coupled with deep learning models such as convolutional neural networks (CNNs) and generative adversarial networks (GANs), to dynamically select the most informative data points. This research is significant because it can lead to substantial reductions in data acquisition costs and time, improving the efficiency of medical imaging, signal processing, and large language model training, among other fields. The ultimate goal is to achieve comparable or even superior results with significantly less data.