# Adversarial Representation Active Learning

arXiv: https://arxiv.org/abs/1912.09720 · HTML: https://mlsanity.com/paper/1912.09720/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2019.

## Built on by (most cited first, 7 of 7)

- [Data-centric Graph Learning: A Survey](https://mlsanity.com/paper/2310.04987/) (2023)
- [A Simple Baseline for Low-Budget Active Learning](https://mlsanity.com/paper/2110.12033/) (2021)
- [Is margin all you need? An extensive empirical study of active learning on tabular data](https://mlsanity.com/paper/2210.03822/) (2022)
- [Making Better Use of Unlabelled Data in Bayesian Active Learning](https://mlsanity.com/paper/2404.17249/) (2024)
- [A Survey on Active Deep Learning: From Model-driven to Data-driven](https://mlsanity.com/paper/2101.09933/) (2021)
- [Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction](https://mlsanity.com/paper/2205.11279/) (2022)
- [ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learning](https://mlsanity.com/paper/2503.07506/) (2025)

## Abstract (arXiv)

Active learning aims to develop label-efficient algorithms by querying the most informative samples to be labeled by an oracle. The design of efficient training methods that require fewer labels is an important research direction that allows more effective use of computational and human resources for labeling and training deep neural networks. In this work, we demonstrate how we can use recent advances in deep generative models, to outperform the state-of-the-art in achieving the highest classification accuracy using as few labels as possible. Unlike previous approaches, our approach uses not only labeled images to train the classifier but also unlabeled images and generated images for co-training the whole model. Our experiments show that the proposed method significantly outperforms existing approaches in active learning on a wide range of datasets (MNIST, CIFAR-10, SVHN, CelebA, and ImageNet).