# Maximally Separated Active Learning

arXiv: https://arxiv.org/abs/2411.17444 · HTML: https://mlsanity.com/paper/2411.17444/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 96 references that are in the map.
- First posted on arXiv 26 Nov 2024.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [Large-Margin Softmax Loss for Convolutional Neural Networks](https://mlsanity.com/paper/1612.02295/) (2016)
- [Adversarial Active Learning for Deep Networks: a Margin Based Approach](https://mlsanity.com/paper/1802.09841/) (2018)
- [Diverse mini-batch Active Learning](https://mlsanity.com/paper/1901.05954/) (2019)
- [A Comparative Survey of Deep Active Learning](https://mlsanity.com/paper/2203.13450/) (2022)
- [Generative Adversarial Active Learning](https://mlsanity.com/paper/1702.07956/) (2017)
- [A Survey of Active Learning for Text Classification using Deep Neural Networks](https://mlsanity.com/paper/2008.07267/) (2020)
- [Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels](https://mlsanity.com/paper/1912.00594/) (2019)
- [Toward a Geometrical Understanding of Self-supervised Contrastive Learning](https://mlsanity.com/paper/2205.06926/) (2022)
- [HYPO: Hyperspherical Out-of-Distribution Generalization](https://mlsanity.com/paper/2402.07785/) (2024)
- [Active Learning for Visual Question Answering: An Empirical Study](https://mlsanity.com/paper/1711.01732/) (2017)
- [Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes](https://mlsanity.com/paper/2107.01622/) (2021)
- [Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient](https://mlsanity.com/paper/2011.13570/) (2020)
- [The Common Stability Mechanism behind most Self-Supervised Learning Approaches](https://mlsanity.com/paper/2402.14957/) (2024)
- [Self-supervised learning with rotation-invariant kernels](https://mlsanity.com/paper/2208.00789/) (2022)

## Abstract (arXiv)

Active Learning aims to optimize performance while minimizing annotation costs by selecting the most informative samples from an unlabelled pool. Traditional uncertainty sampling often leads to sampling bias by choosing similar uncertain samples. We propose an active learning method that utilizes fixed equiangular hyperspherical points as class prototypes, ensuring consistent inter-class separation and robust feature representations. Our approach introduces Maximally Separated Active Learning (MSAL) for uncertainty sampling and a combined strategy (MSAL-D) for incorporating diversity. This method eliminates the need for costly clustering steps, while maintaining diversity through hyperspherical uniformity. We demonstrate strong performance over existing active learning techniques across five benchmark datasets, highlighting the method's effectiveness and integration ease. The code is available on GitHub.