Diverse mini-batch Active Learning

TL;DR

This paper proposes a scalable mini-batch active learning method that combines informativeness and diversity, improving data efficiency for training deep learning models.

Contribution

It introduces a novel mini-batch active learning approach using K-means clustering to balance informativeness and diversity, enhancing scalability and performance.

Findings

Achieves comparable or better performance than existing methods.

Scales efficiently with large datasets.

Effectively balances informativeness and diversity in mini-batch selection.

Abstract

We study the problem of reducing the amount of labeled training data required to train supervised classification models. We approach it by leveraging Active Learning, through sequential selection of examples which benefit the model most. Selecting examples one by one is not practical for the amount of training examples required by the modern Deep Learning models. We consider the mini-batch Active Learning setting, where several examples are selected at once. We present an approach which takes into account both informativeness of the examples for the model, as well as the diversity of the examples in a mini-batch. By using the well studied K-means clustering algorithm, this approach scales better than the previously proposed approaches, and achieves comparable or better performance.