Improving performance in multiple domains is a challenging task, and often
requires significant amounts of data to train and test models. Active learning
techniques provide a promising solution by enabling models to select the most
informative samples for labeling, thus reducing the amount of labeled data
required to achieve high performance. In this paper, we present an active
learning-based framework for improving performance across multiple domains. Our
approach consists of two stages: first, we use an initial set of labeled data
to train a base model, and then we iteratively select the most informative
samples for labeling to refine the model. We evaluate our approach on several
multi-domain datasets, including image classification, sentiment analysis, and
object recognition. Our experiments demonstrate that our approach consistently
outperforms baseline methods and achieves state-of-the-art performance on
several datasets. We also show that our method is highly efficient, requiring
significantly fewer labeled samples than other active learning-based methods.
Overall, our approach provides a practical and effective solution for improving
performance across multiple domains using active learning techniques.

本篇论文介绍了一种基于主动学习的多领域性能提升框架，首先利用初始标记数据对基础模型进行训练，然后迭代地选择最具信息量的样本进行标记，以改进模型性能。该方法在图像分类、情感分析和物体识别等多领域数据集上进行了评估，并表现出超越基准方法，并在几个数据集上取得了最先进的性能。同时表明了本方法非常高效，比其他主动学习方法需要明显较少的标记样本，是一种实用且有效的多领域性能提升解决方案。

利用基于主动学习的改进策略优化多域性能

Optimizing Multi-Domain Performance with Active Learning-based  Improvement Strategies

Selective prediction aims to learn a reliable model that abstains from making
predictions when the model uncertainty is high. These predictions can then be
deferred to a human expert for further evaluation. In many real-world
scenarios, however, the distribution of test data is different from the
training data. This results in more inaccurate predictions, necessitating
increased human labeling, which is difficult and expensive in many scenarios.
Active learning circumvents this difficulty by only querying the most
informative examples and, in several cases, has been shown to lower the overall
labeling effort. In this work, we bridge the gap between selective prediction
and active learning, proposing a new learning paradigm called active selective
prediction which learns to query more informative samples from the shifted
target domain while increasing accuracy and coverage. For this new problem, we
propose a simple but effective solution, ASPEST, that trains ensembles of model
snapshots using self-training with their aggregated outputs as pseudo labels.
Extensive experiments on several image, text and structured datasets with
domain shifts demonstrate that active selective prediction can significantly
outperform prior work on selective prediction and active learning (e.g. on the
MNIST$\to$SVHN benchmark with the labeling budget of 100, ASPEST improves the
AUC metric from 79.36% to 88.84%) and achieves more optimal utilization of
humans in the loop.

本篇论文提出了一种新的学习范式 “主动选择性预测”，利用自训练生成擬标签的方法提高预测精度并在更多数据上利用人的力量，成功地将选择性预测和主动学习二者结合起来，获得比以往更好的性能。

ASPEST：主动学习与选择性预测之间的桥梁

ASPEST: Bridging the Gap Between Active Learning and Selective  Prediction

In training speech recognition systems, labeling audio clips can be
expensive, and not all data is equally valuable. Active learning aims to label
only the most informative samples to reduce cost. For speech recognition,
confidence scores and other likelihood-based active learning methods have been
shown to be effective. Gradient-based active learning methods, however, are
still not well-understood. This work investigates the Expected Gradient Length
(EGL) approach in active learning for end-to-end speech recognition. We justify
EGL from a variance reduction perspective, and observe that EGL's measure of
informativeness picks novel samples uncorrelated with confidence scores.
Experimentally, we show that EGL can reduce word errors by 11\%, or
alternatively, reduce the number of samples to label by 50\%, when compared to
random sampling.

本研究中，我们探究了基于梯度的主动学习方法 —— 期望梯度长度（EGL）在端到端语音识别中的应用，从方差减少的角度证明了 EGL 的有效性，并且实验证明，与随机采样相比，EGL 能够减少 50% 的标签样本或者将单词误差率降低 11%。