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。

利用不确定性和多样抽样的两步主动学习实例分割

Two-Step Active Learning for Instance Segmentation with Uncertainty and  Diversity Sampling

Self-training has proven effective for improving NMT performance by
augmenting model training with synthetic parallel data. The common practice is
to construct synthetic data based on a randomly sampled subset of large-scale
monolingual data, which we empirically show is sub-optimal. In this work, we
propose to improve the sampling procedure by selecting the most informative
monolingual sentences to complement the parallel data. To this end, we compute
the uncertainty of monolingual sentences using the bilingual dictionary
extracted from the parallel data. Intuitively, monolingual sentences with lower
uncertainty generally correspond to easy-to-translate patterns which may not
provide additional gains. Accordingly, we design an uncertainty-based sampling
strategy to efficiently exploit the monolingual data for self-training, in
which monolingual sentences with higher uncertainty would be sampled with
higher probability. Experimental results on large-scale WMT
English$\Rightarrow$German and English$\Rightarrow$Chinese datasets demonstrate
the effectiveness of the proposed approach. Extensive analyses suggest that
emphasizing the learning on uncertain monolingual sentences by our approach
does improve the translation quality of high-uncertainty sentences and also
benefits the prediction of low-frequency words at the target side.

本文提出了一种基于不确定性采样的自训练方法，通过选择最具信息价值的单一语言句子来补充平行数据，以提高 NMT 的性能，在大规模数据集上进行实验证明了这种方法的有效性，并表明该方法能够提高翻译质量和预测低频单词。