# A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text Data

arXiv: https://arxiv.org/abs/2109.03784 · HTML: https://mlsanity.com/paper/2109.03784/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 95 references that are in the map.
- First posted on arXiv 8 Sep 2021.

## Builds on (most cited first, 13 of 13)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://mlsanity.com/paper/1603.05279/) (2016)
- [Rethinking ImageNet Pre-training](https://mlsanity.com/paper/1811.08883/) (2018)
- [What makes ImageNet good for transfer learning?](https://mlsanity.com/paper/1608.08614/) (2016)
- [Towards the first adversarially robust neural network model on MNIST](https://mlsanity.com/paper/1805.09190/) (2018)
- [Comparison of non-linear activation functions for deep neural networks on MNIST classification task](https://mlsanity.com/paper/1804.02763/) (2018)
- [ScribbleBox: Interactive Annotation Framework for Video Object Segmentation](https://mlsanity.com/paper/2008.09721/) (2020)
- [Transmitter Classification With Supervised Deep Learning](https://mlsanity.com/paper/1905.07923/) (2019)
- [audino: A Modern Annotation Tool for Audio and Speech](https://mlsanity.com/paper/2006.05236/) (2020)
- [Automatic Pass Annotation from Soccer VideoStreams Based on Object Detection and LSTM](https://mlsanity.com/paper/2007.06475/) (2020)

## Built on by (most cited first, 6 of 6)

- [Enhancing Vehicle Environmental Awareness via Federated Learning and Automatic Labeling](https://mlsanity.com/paper/2408.12769/) (2024)
- [Trimming the Risk: Towards Reliable Continuous Training for Deep Learning Inspection Systems](https://mlsanity.com/paper/2409.09108/) (2024)
- [K-GBS3FCM -- KNN Graph-Based Safe Semi-Supervised Fuzzy C-Means](https://mlsanity.com/paper/2411.14728/) (2024)
- [BakuFlow: A Streamlining Semi-Automatic Label Generation Tool](https://mlsanity.com/paper/2506.09083/) (2025)
- [PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data](https://mlsanity.com/paper/2508.15180/) (2025)
- [Active Transfer Bagging: A New Approach for Accelerated Active Learning Acquisition of Data by Combined Transfer Learning and Bagging Based Models](https://mlsanity.com/paper/2602.02415/) (2026)

## Abstract (arXiv)

Machine learning has been utilized to perform tasks in many different domains such as classification, object detection, image segmentation and natural language analysis. Data labeling has always been one of the most important tasks in machine learning. However, labeling large amounts of data increases the monetary cost in machine learning. As a result, researchers started to focus on reducing data annotation and labeling costs. Transfer learning was designed and widely used as an efficient approach that can reasonably reduce the negative impact of limited data, which in turn, reduces the data preparation cost. Even transferring previous knowledge from a source domain reduces the amount of data needed in a target domain. However, large amounts of annotated data are still demanded to build robust models and improve the prediction accuracy of the model. Therefore, researchers started to pay more attention on auto annotation and labeling. In this survey paper, we provide a review of previous techniques that focuses on optimized data annotation and labeling for video, audio, and text data.