This study addresses the integration of diversity-based and uncertainty-based
sampling strategies in active learning, particularly within the context of
self-supervised pre-trained models. We introduce a straightforward heuristic
called TCM that mitigates the cold start problem while maintaining strong
performance across various data levels. By initially applying TypiClust for
diversity sampling and subsequently transitioning to uncertainty sampling with
Margin, our approach effectively combines the strengths of both strategies. Our
experiments demonstrate that TCM consistently outperforms existing methods
across various datasets in both low and high data regimes.

使用 TCM 方法，将多样性采样和不确定性采样策略整合到主动学习中，解决了冷启动问题，在低数据和高数据情况下均表现出卓越的性能。

主动学习中的多样性和不确定性的桥接与自监督预训练

Bridging Diversity and Uncertainty in Active learning with  Self-Supervised Pre-Training

Training high-quality instance segmentation models requires an abundance of
labeled images with instance masks and classifications, which is often
expensive to procure. Active learning addresses this challenge by striving for
optimum performance with minimal labeling cost by selecting the most
informative and representative images for labeling. Despite its potential,
active learning has been less explored in instance segmentation compared to
other tasks like image classification, which require less labeling. In this
study, we propose a post-hoc active learning algorithm that integrates
uncertainty-based sampling with diversity-based sampling. Our proposed
algorithm is not only simple and easy to implement, but it also delivers
superior performance on various datasets. Its practical application is
demonstrated on a real-world overhead imagery dataset, where it increases the
labeling efficiency fivefold.

我们提出了一种后期主动学习算法，该算法将基于不确定性的采样与基于多样性的采样相结合，不仅简单易实现，而且在各种数据集上都具有出色的表现，其实际应用在实际的高空图像数据集中增加了五倍的标注效率。