# The Effectiveness of Variational Autoencoders for Active Learning

arXiv: https://arxiv.org/abs/1911.07716 · HTML: https://mlsanity.com/paper/1911.07716/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Deep Diffusion Processes for Active Learning of Hyperspectral Images](https://mlsanity.com/paper/2101.03197/) (2021)
- [Task-specific experimental design for treatment effect estimation](https://mlsanity.com/paper/2306.05484/) (2023)

## Abstract (arXiv)

The high cost of acquiring labels is one of the main challenges in deploying supervised machine learning algorithms. Active learning is a promising approach to control the learning process and address the difficulties of data labeling by selecting labeled training examples from a large pool of unlabeled instances. In this paper, we propose a new data-driven approach to active learning by choosing a small set of labeled data points that are both informative and representative. To this end, we present an efficient geometric technique to select a diverse core-set in a low-dimensional latent space obtained by training a Variational Autoencoder (VAE). Our experiments demonstrate an improvement in accuracy over two related techniques and, more importantly, signify the representation power of generative modeling for developing new active learning methods in high-dimensional data settings.