The success of deep learning hinges on enormous data and large models, which
require labor-intensive annotations and heavy computation costs. Subset
selection is a fundamental problem that can play a key role in identifying
smaller portions of the training data, which can then be used to produce
similar models as the ones trained with full data. Two prior methods are shown
to achieve impressive results: (1) margin sampling that focuses on selecting
points with high uncertainty, and (2) core-sets or clustering methods such as
k-center for informative and diverse subsets. We are not aware of any work that
combines these methods in a principled manner. To this end, we develop a novel
and efficient factor 3-approximation algorithm to compute subsets based on the
weighted sum of both k-center and uncertainty sampling objective functions. To
handle large datasets, we show a parallel algorithm to run on multiple machines
with approximation guarantees. The proposed algorithm achieves similar or
better performance compared to other strong baselines on vision datasets such
as CIFAR-10, CIFAR-100, and ImageNet.

基于深度学习的子集选择方法，通过结合高不确定性的边缘采样和多样性聚类方法的加权和来计算子集，并通过并行算法在大数据集上取得了类似或更好的性能表现。

一种基于加权 K-Center 算法的数据子集选择

A Weighted K-Center Algorithm for Data Subset Selection

Given a labeled training set and a collection of unlabeled data, the goal of
active learning (AL) is to identify the best unlabeled points to label. In this
comprehensive study, we analyze the performance of a variety of AL algorithms
on deep neural networks trained on 69 real-world tabular classification
datasets from the OpenML-CC18 benchmark. We consider different data regimes and
the effect of self-supervised model pre-training. Surprisingly, we find that
the classical margin sampling technique matches or outperforms all others,
including current state-of-art, in a wide range of experimental settings. To
researchers, we hope to encourage rigorous benchmarking against margin, and to
practitioners facing tabular data labeling constraints that
hyper-parameter-free margin may often be all they need.

该研究综合分析了在 69 个现实世界的标签分类数据集上，使用各种主动学习算法在深度神经网络上的表现，结果表明，基于经典边际采样技术的算法在各种实验设置下都能匹配或超越其他算法，甚至包括当前的最佳算法。

利润是否就足够了？基于表格数据的主动学习的广泛经验研究

Is margin all you need? An extensive empirical study of active learning on tabular data

Recent years have witnessed the quick progress of the hyperspectral images
(HSI) classification. Most of existing studies either heavily rely on the
expensive label information using the supervised learning or can hardly exploit
the discriminative information borrowed from related domains. To address this
issues, in this paper we show a novel framework addressing HSI classification
based on the domain adaptation (DA) with active learning (AL). The main idea of
our method is to retrain the multi-kernel classifier by utilizing the available
labeled samples from source domain, and adding minimum number of the most
informative samples with active queries in the target domain. The proposed
method adaptively combines multiple kernels, forming a DA classifier that
minimizes the bias between the source and target domains. Further equipped with
the nested actively updating process, it sequentially expands the training set
and gradually converges to a satisfying level of classification performance. We
study this active adaptation framework with the Margin Sampling (MS) strategy
in the HSI classification task. Our experimental results on two popular HSI
datasets demonstrate its effectiveness.

本文提出了一个新的基于领域适应和主动学习的超光谱图像分类框架，通过利用源域中可用的标记样本重新训练多核分类器，并添加少量的目标域中的最具信息量的样本，逐步扩大训练集，适应性地组合多个内核，形成最小化源域和目标域之间偏差的 DA 分类器。使用 Margin Sampling 策略在 HSI 分类任务中对主动适应框架进行研究。实验结果表明其有效性。