Convolutional Neural Networks (CNNs) have proven to be state-of-the-art
models for supervised computer vision tasks, such as image classification.
However, large labeled data sets are generally needed for the training and
validation of such models. In many domains, unlabeled data is available but
labeling is expensive, for instance when specific expert knowledge is required.
Active Learning (AL) is one approach to mitigate the problem of limited labeled
data. Through selecting the most informative and representative data instances
for labeling, AL can contribute to more efficient learning of the model. Recent
AL methods for CNNs propose different solutions for the selection of instances
to be labeled. However, they do not perform consistently well and are often
computationally expensive. In this paper, we propose a novel AL algorithm that
efficiently learns from unlabeled data by capturing high prediction
uncertainty. By replacing the softmax standard output of a CNN with the
parameters of a Dirichlet density, the model learns to identify data instances
that contribute efficiently to improving model performance during training. We
demonstrate in several experiments with publicly available data that our method
consistently outperforms other state-of-the-art AL approaches. It can be easily
implemented and does not require extensive computational resources for
training. Additionally, we are able to show the benefits of the approach on a
real-world medical use case in the field of automated detection of visual
signals for pneumonia on chest radiographs.

本文提出一种新的主动学习算法，将 CNN 模型的输出从 softmax 替换为 Dirichlet 值，实现从未标记数据中提取最具信息量的标记数据集，该方法在多项数据集和医学图像识别领域中与其他主动学习方法相比均具有更高的性能表现和易于实现且不需要大量计算资源的优点。