# Data Collection and Labeling Techniques for Machine Learning

arXiv: https://arxiv.org/abs/2407.12793 · HTML: https://mlsanity.com/paper/2407.12793/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 68 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/1906.08158/) (2019)
- [CTAB-GAN: Effective Table Data Synthesizing](https://mlsanity.com/paper/2102.08369/) (2021)
- [MedGAN: Medical Image Translation using GANs](https://mlsanity.com/paper/1806.06397/) (2018)
- [Self-Driving Cars: A Survey](https://mlsanity.com/paper/1901.04407/) (2019)
- [A Survey of Label-noise Representation Learning: Past, Present and Future](https://mlsanity.com/paper/2011.04406/) (2020)
- [Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning](https://mlsanity.com/paper/1912.10389/) (2019)
- [A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective](https://mlsanity.com/paper/1811.03402/) (2018)
- [A Survey on Programmatic Weak Supervision](https://mlsanity.com/paper/2202.05433/) (2022)
- [ZeroER: Entity Resolution using Zero Labeled Examples](https://mlsanity.com/paper/1908.06049/) (2019)
- [In Defense of Core-set: A Density-aware Core-set Selection for Active Learning](https://mlsanity.com/paper/2206.04838/) (2022)
- [Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions](https://mlsanity.com/paper/2005.05117/) (2020)
- [Open Knowledge Enrichment for Long-tail Entities](https://mlsanity.com/paper/2002.06397/) (2020)
- [DataHub: Collaborative Data Science & Dataset Version Management at Scale](https://mlsanity.com/paper/1409.0798/) (2014)
- [Demonstration of Panda: A Weakly Supervised Entity Matching System](https://mlsanity.com/paper/2106.10821/) (2021)
- [Ground Truth Inference for Weakly Supervised Entity Matching](https://mlsanity.com/paper/2211.06975/) (2022)

## Built on by (most cited first, 4 of 4)

- [RawMal-TF: Raw Malware Dataset Labeled by Type and Family](https://mlsanity.com/paper/2506.23909/) (2025)
- [Human services organizations and the responsible integration of AI: Considering ethics and contextualizing risk(s)](https://mlsanity.com/paper/2501.11705/) (2025)
- [Integration of TinyML and LargeML: A Survey of 6G and Beyond](https://mlsanity.com/paper/2505.15854/) (2025)
- [Label Curation Using Agentic AI](https://mlsanity.com/paper/2602.02564/) (2026)

## Abstract (arXiv)

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.