Data Collection and Labeling Techniques for Machine Learning
It addresses the problem of efficient and scalable data handling for machine learning practitioners, but it is incremental as it reviews existing techniques.
This paper reviews state-of-the-art methods for data collection and labeling, which are critical bottlenecks in deploying machine learning applications, aiming to provide a holistic view and identify future research directions.
Data collection and labeling are critical bottlenecks in the deployment of machine learning applications. With the increasing complexity and diversity of applications, the need for efficient and scalable data collection and labeling techniques has become paramount. This paper provides a review of the state-of-the-art methods in data collection, data labeling, and the improvement of existing data and models. By integrating perspectives from both the machine learning and data management communities, we aim to provide a holistic view of the current landscape and identify future research directions.