Data Collection and Labeling Techniques for Machine Learning

TL;DR

This paper reviews current data collection and labeling techniques for machine learning, emphasizing their importance, challenges, and future research directions to improve scalability and efficiency.

Contribution

It offers a comprehensive overview integrating machine learning and data management perspectives, highlighting recent advances and identifying gaps for future research.

Findings

Survey of state-of-the-art data collection methods

Analysis of data labeling techniques and challenges

Identification of future research directions

Abstract

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.