Targeting Negative Flips in Active Learning using Validation Sets

TL;DR

This paper introduces ROSE, a novel active learning method using validation sets to reduce negative flips and improve accuracy, revealing that these two metrics are decoupled and can be optimized independently.

Contribution

The paper presents ROSE, a plug-in algorithm that leverages validation sets to directly target negative flips, enhancing active learning performance beyond traditional methods.

Findings

Negative flips and overall error rates are decoupled.

Targeted active learning on subsets impacts both metrics.

ROSE improves accuracy and reduces negative flips significantly.

Abstract

The performance of active learning algorithms can be improved in two ways. The often used and intuitive way is by reducing the overall error rate within the test set. The second way is to ensure that correct predictions are not forgotten when the training set is increased in between rounds. The former is measured by the accuracy of the model and the latter is captured in negative flips between rounds. Negative flips are samples that are correctly predicted when trained with the previous/smaller dataset and incorrectly predicted after additional samples are labeled. In this paper, we discuss improving the performance of active learning algorithms both in terms of prediction accuracy and negative flips. The first observation we make in this paper is that negative flips and overall error rates are decoupled and reducing one does not necessarily imply that the other is reduced. Our observation is important as current active learning algorithms do not consider negative flips directly and implicitly assume the opposite. The second observation is that performing targeted active learning on subsets of the unlabeled pool has a significant impact on the behavior of the active learning algorithm and influences both negative flips and prediction accuracy. We then develop ROSE - a plug-in algorithm that utilizes a small labeled validation set to restrict arbitrary active learning acquisition functions to negative flips within the unlabeled pool. We show that integrating a validation set results in a significant performance boost in terms of accuracy, negative flip rate reduction, or both.