On the Robustness of Active Learning

TL;DR

This paper analyzes the robustness of various active learning methods across different classifiers, hyperparameters, and data conditions, revealing biases and proposing a new sample selection technique based on diversity indices.

Contribution

It provides a comprehensive robustness analysis of active learning methods and introduces the 'Sum of Squared Logits' method for improved sample selection.

Findings

Active learning methods show biases towards specific classifiers.

Classifier performance varies with hyperparameters and data quality.

The proposed 'Sum of Squared Logits' method enhances sample diversity.

Abstract

Active Learning is concerned with the question of how to identify the most useful samples for a Machine Learning algorithm to be trained with. When applied correctly, it can be a very powerful tool to counteract the immense data requirements of Artificial Neural Networks. However, we find that it is often applied with not enough care and domain knowledge. As a consequence, unrealistic hopes are raised and transfer of the experimental results from one dataset to another becomes unnecessarily hard. In this work we analyse the robustness of different Active Learning methods with respect to classifier capacity, exchangeability and type, as well as hyperparameters and falsely labelled data. Experiments reveal possible biases towards the architecture used for sample selection, resulting in suboptimal performance for other classifiers. We further propose the new "Sum of Squared Logits" method based on the Simpson diversity index and investigate the effect of using the confusion matrix for balancing in sample selection.