# Single Shot Active Learning using Pseudo Annotators

arXiv: https://arxiv.org/abs/1805.06660 · HTML: https://mlsanity.com/paper/1805.06660/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2018.

## Built on by (most cited first, 9 of 9)

- [A Survey on Deep Active Learning: Recent Advances and New Frontiers](https://mlsanity.com/paper/2405.00334/) (2024)
- [Batch Active Learning of Reward Functions from Human Preferences](https://mlsanity.com/paper/2402.15757/) (2024)
- [A Survey of Dataset Refinement for Problems in Computer Vision Datasets](https://mlsanity.com/paper/2210.11717/) (2022)
- [A Survey on Active Deep Learning: From Model-driven to Data-driven](https://mlsanity.com/paper/2101.09933/) (2021)
- [MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.14339/) (2021)
- [Active Self-Semi-Supervised Learning for Few Labeled Samples](https://mlsanity.com/paper/2203.04560/) (2022)
- [One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models](https://mlsanity.com/paper/2405.14121/) (2024)
- [Batch Active Learning from the Perspective of Sparse Approximation](https://mlsanity.com/paper/2211.00246/) (2022)
- [Deep Active Learning with Budget Annotation](https://mlsanity.com/paper/2208.00508/) (2022)

## Abstract (arXiv)

Standard myopic active learning assumes that human annotations are always obtainable whenever new samples are selected. This, however, is unrealistic in many real-world applications where human experts are not readily available at all times. In this paper, we consider the single shot setting: all the required samples should be chosen in a single shot and no human annotation can be exploited during the selection process. We propose a new method, Active Learning through Random Labeling (ALRL), which substitutes single human annotator for multiple, what we will refer to as, pseudo annotators. These pseudo annotators always provide uniform and random labels whenever new unlabeled samples are queried. This random labeling enables standard active learning algorithms to also exhibit the exploratory behavior needed for single shot active learning. The exploratory behavior is further enhanced by selecting the most representative sample via minimizing nearest neighbor distance between unlabeled samples and queried samples. Experiments on real-world datasets demonstrate that the proposed method outperforms several state-of-the-art approaches.