Active learning (AL) is an effective approach to select the most informative
samples to label so as to reduce the annotation cost. Existing AL methods
typically work under the closed-set assumption, i.e., all classes existing in
the unlabeled sample pool need to be classified by the target model. However,
in some practical clinical tasks, the unlabeled pool may contain not only the
target classes that need to be fine-grainedly classified, but also non-target
classes that are irrelevant to the clinical tasks. Existing AL methods cannot
work well in this scenario because they tend to select a large number of
non-target samples. In this paper, we formulate this scenario as an open-set AL
problem and propose an efficient framework, OpenAL, to address the challenge of
querying samples from an unlabeled pool with both target class and non-target
class samples. Experiments on fine-grained classification of pathology images
show that OpenAL can significantly improve the query quality of target class
samples and achieve higher performance than current state-of-the-art AL
methods. Code is available at this https URL

本文提出一种解决在打标签样本池中既包含目标类和非目标类的情况下如何查询样本的问题的方法 ——OpenAL，并在细粒度分类病理图像的实验中表明其可以显著提高目标类样本的查询质量，达到比当前最先进的主动学习方法更高的性能。

OpenAL: 用于开放式病理图像分类的高效深度主动学习框架

OpenAL: An Efficient Deep Active Learning Framework for Open-Set  Pathology Image Classification

Since annotating and curating large datasets is very expensive, there is a
need to transfer the knowledge from existing annotated datasets to unlabelled
data. Data that is relevant for a specific application, however, usually
differs from publicly available datasets since it is sampled from a different
domain. While domain adaptation methods compensate for such a domain shift,
they assume that all categories in the target domain are known and match the
categories in the source domain. Since this assumption is violated under
real-world conditions, we propose an approach for open set domain adaptation
where the target domain contains instances of categories that are not present
in the source domain. The proposed approach achieves state-of-the-art results
on various datasets for image classification and action recognition. Since the
approach can be used for open set and closed set domain adaptation, as well as
unsupervised and semi-supervised domain adaptation, it is a versatile tool for
many applications.

提出了一种应对开放域数据集且能用于无监督和半监督领域自适应的方法，该方法在图像分类和动作识别等任务上实现了最先进的结果。