Dataset Growth

TL;DR

This paper introduces InfoGrowth, an online algorithm designed to efficiently clean and select data in real-time, addressing the challenges of exponential data growth and quality management for deep learning applications.

Contribution

The paper presents a novel online data cleaning and selection algorithm that maintains dataset quality and diversity amidst rapidly growing data sources.

Findings

InfoGrowth improves data quality and efficiency for deep learning tasks.

It is scalable and effective for both single-modal and multi-modal data.

The framework is practical for real-world data management systems.

Abstract

Deep learning benefits from the growing abundance of available data. Meanwhile, efficiently dealing with the growing data scale has become a challenge. Data publicly available are from different sources with various qualities, and it is impractical to do manual cleaning against noise and redundancy given today's data scale. There are existing techniques for cleaning/selecting the collected data. However, these methods are mainly proposed for offline settings that target one of the cleanness and redundancy problems. In practice, data are growing exponentially with both problems. This leads to repeated data curation with sub-optimal efficiency. To tackle this challenge, we propose InfoGrowth, an efficient online algorithm for data cleaning and selection, resulting in a growing dataset that keeps up to date with awareness of cleanliness and diversity. InfoGrowth can improve data quality/efficiency on both single-modal and multi-modal tasks, with an efficient and scalable design. Its framework makes it practical for real-world data engines.