# Noisy Batch Active Learning with Deterministic Annealing

arXiv: https://arxiv.org/abs/1909.12473 · HTML: https://mlsanity.com/paper/1909.12473/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2019.

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

- [Active Learning for Argument Mining: A Practical Approach](https://mlsanity.com/paper/2109.13611/) (2021)
- [Active Learning with Imperfect Labels: Optimal Labeler Assignment and Sample Selection](https://mlsanity.com/paper/2512.12870/) (2025)

## Abstract (arXiv)

We study the problem of training machine learning models incrementally with batches of samples annotated with noisy oracles. We select each batch of samples that are important and also diverse via clustering and importance sampling. More importantly, we incorporate model uncertainty into the sampling probability to compensate for poor estimation of the importance scores when the training data is too small to build a meaningful model. Experiments on benchmark image classification datasets (MNIST, SVHN, CIFAR10, and EMNIST) show improvement over existing active learning strategies. We introduce an extra denoising layer to deep networks to make active learning robust to label noises and show significant improvements.