Optimizing Active Learning for Low Annotation Budgets

TL;DR

This paper proposes a transfer learning-inspired active learning approach for deep models that reduces annotation costs by using a pre-trained feature extractor and a novel acquisition function based on model prediction shifts.

Contribution

It introduces a new active learning method that combines transfer learning and a robust sample selection strategy to improve performance with limited annotation budgets.

Findings

Outperforms existing methods on balanced datasets

Effective in imbalanced dataset scenarios

Reduces initial annotation requirements

Abstract

When we can not assume a large amount of annotated data , active learning is a good strategy. It consists in learning a model on a small amount of annotated data (annotation budget) and in choosing the best set of points to annotate in order to improve the previous model and gain in generalization. In deep learning, active learning is usually implemented as an iterative process in which successive deep models are updated via fine tuning, but it still poses some issues. First, the initial batch of annotated images has to be sufficiently large to train a deep model. Such an assumption is strong, especially when the total annotation budget is reduced. We tackle this issue by using an approach inspired by transfer learning. A pre-trained model is used as a feature extractor and only shallow classifiers are learned during the active iterations. The second issue is the effectiveness of probability or feature estimates of early models for AL task. Samples are generally selected for annotation using acquisition functions based only on the last learned model. We introduce a novel acquisition function which exploits the iterative nature of AL process to select samples in a more robust fashion. Samples for which there is a maximum shift towards uncertainty between the last two learned models predictions are favored. A diversification step is added to select samples from different regions of the classification space and thus introduces a representativeness component in our approach. Evaluation is done against competitive methods with three balanced and imbalanced datasets and outperforms them.