Active Acquisition

Active acquisition focuses on strategically selecting the most informative data points for model training, minimizing data acquisition costs while maximizing model performance. Current research explores this across diverse domains, employing techniques like adaptive compressed sensing with diffusion models for efficient image acquisition, and leveraging large language models and other machine learning architectures to guide data selection in robotics and other applications. This research is significant for accelerating data-intensive tasks in fields like medical imaging, robotics, and marketing, enabling faster, cheaper, and more efficient model development and deployment.