Most of the existing learning models, particularly deep neural networks, are reliant on large datasets whose hand-labeling is expensive and time demanding. A current trend is to make the learning of these models frugal and less dependent on large collections of labeled data. Among the existing solutions, deep active learning is currently witnessing a major interest and its purpose is to train deep networks using as few labeled samples as possible. However, the success of active learning is highly dependent on how critical are these samples when training models. In this paper, we devise a novel active learning approach for label-efficient training. The proposed method is iterative and aims at minimizing a constrained objective function that mixes diversity, representativity and uncertainty criteria. The proposed approach is probabilistic and unifies all these criteria in a single objective function whose solution models the probability of relevance of samples (i.e., how critical) when learning a decision function. We also introduce a novel weighting mechanism based on reinforcement learning, which adaptively balances these criteria at each training iteration, using a particular stateless Q-learning model. Extensive experiments conducted on staple image classification data, including Object-DOTA, show the effectiveness of our proposed model w.r.t. several baselines including random, uncertainty and flat as well as other work.

Satellite image change detection aims at finding occurrences of targeted changes in a given scene taken at different instants. This task is highly challenging due to the acquisition conditions and also to the subjectivity of changes. In this paper, we investigate satellite image change detection using active learning. Our method is interactive and relies on a question and answer model which asks the oracle (user) questions about the most informative display (dubbed as virtual exemplars), and according to the user's responses, updates change detections. The main contribution of our method consists in a novel adversarial model that allows frugally probing the oracle with only the most representative, diverse and uncertain virtual exemplars. The latter are learned to challenge the most the trained change decision criteria which ultimately leads to a better re-estimate of these criteria in the following iterations of active learning. Conducted experiments show the out-performance of our proposed adversarial display model against other display strategies as well as the related work.

As an important data selection schema, active learning emerges as the essential component when iterating an Artificial Intelligence (AI) model. It becomes even more critical given the dominance of deep neural network based models, which are composed of a large number of parameters and data hungry, in application. Despite its indispensable role for developing AI models, research on active learning is not as intensive as other research directions. In this paper, we present a review of active learning through deep active learning approaches from the following perspectives: 1) technical advancements in active learning, 2) applications of active learning in computer vision, 3) industrial systems leveraging or with potential to leverage active learning for data iteration, 4) current limitations and future research directions. We expect this paper to clarify the significance of active learning in a modern AI model manufacturing process and to bring additional research attention to active learning. By addressing data automation challenges and coping with automated machine learning systems, active learning will facilitate democratization of AI technologies by boosting model production at scale.

本文解决了几秒钟学习问题，旨在从几个例子中学习新的视觉概念。在几次拍摄分类中的常见问题设置假设在获取数据标签中的随机采样策略，其在实际应用中效率低下。在这项工作中，我们介绍了一个新的预算感知几秒钟学习问题，不仅旨在学习新的对象类别，还需要选择信息实例来注释以实现数据效率。我们为我们的预算感知几秒钟学习任务开发了一个元学习策略，该任务共同了解基于图形卷积网络（GCN）和基于示例的少量拍摄分类器的新型数据选择策略。我们的选择策略通过图形消息传递计算每个未标记数据的上下文敏感表示，然后用于预测顺序选择的信息性分数。我们在迷你想象网，分层 - 想象项目和omniglot数据集上进行广泛的实验验证我们的方法。结果表明，我们的几次学习策略优于一个相当大的边缘，这表明了我们的方法的功效。

While deep learning succeeds in a wide range of tasks, it highly depends on the massive collection of annotated data which is expensive and time-consuming. To lower the cost of data annotation, active learning has been proposed to interactively query an oracle to annotate a small proportion of informative samples in an unlabeled dataset. Inspired by the fact that the samples with higher loss are usually more informative to the model than the samples with lower loss, in this paper we present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss. The core of our approach is a measurement Temporal Output Discrepancy (TOD) that estimates the sample loss by evaluating the discrepancy of outputs given by models at different optimization steps. Our theoretical investigation shows that TOD lower-bounds the accumulated sample loss thus it can be used to select informative unlabeled samples. On basis of TOD, we further develop an effective unlabeled data sampling strategy as well as an unsupervised learning criterion for active learning. Due to the simplicity of TOD, our methods are efficient, flexible, and task-agnostic. Extensive experimental results demonstrate that our approach achieves superior performances than the state-of-the-art active learning methods on image classification and semantic segmentation tasks. In addition, we show that TOD can be utilized to select the best model of potentially the highest testing accuracy from a pool of candidate models.

在研究积极的学习时，我们专注于标记的示例数量（预算规模）和合适的查询策略之间的关系。我们的理论分析表明，一种让人联想到相变的行为：预算低时最好查询典型的示例，而预算较大时最好查询无代表性的示例。合并的证据表明，类似的现象发生在共同的分类模型中。因此，我们提出了典型lust，这是一种适合低预算的深度积极学习策略。在对监督学习的比较实证研究中，使用各种架构和图像数据集，TypicLust在低预算制度中的所有其他活跃学习策略都优于所有其他活跃的学习策略。在半监督框架中使用TypicLust，性能得到更加显着的提升。特别是，在CIFAR-10上训练的最新半监督方法，由Typiclust选择的10个标记的示例训练，达到93.2％的精度 - 比随机选择提高了39.4％。代码可在https://github.com/avihu111/typiclust上找到。

本文解决了在水模型部署民主化中采用了机器学习的一些挑战。第一个挑战是减少了在主动学习的帮助下减少了标签努力（因此关注数据质量），模型推断与Oracle之间的反馈循环：如在保险中，未标记的数据通常丰富，主动学习可能会成为一个重要的资产减少标签成本。为此目的，本文在研究其对合成和真实数据集的实证影响之前，阐述了各种古典主动学习方法。保险中的另一个关键挑战是模型推论中的公平问题。我们将在此主动学习框架中介绍和整合一个用于多级任务的后处理公平，以解决这两个问题。最后对不公平数据集的数值实验突出显示所提出的设置在模型精度和公平性之间存在良好的折衷。

主动学习（al）试图通过标记最少的样本来最大限度地提高模型的性能增益。深度学习（DL）是贪婪的数据，需要大量的数据电源来优化大量参数，因此模型了解如何提取高质量功能。近年来，由于互联网技术的快速发展，我们处于信息种类的时代，我们有大量的数据。通过这种方式，DL引起了研究人员的强烈兴趣，并已迅速发展。与DL相比，研究人员对Al的兴趣相对较低。这主要是因为在DL的崛起之前，传统的机器学习需要相对较少的标记样品。因此，早期的Al很难反映其应得的价值。虽然DL在各个领域取得了突破，但大多数这一成功都是由于大量现有注释数据集的宣传。然而，收购大量高质量的注释数据集消耗了很多人力，这在某些领域不允许在需要高专业知识，特别是在语音识别，信息提取，医学图像等领域中， al逐渐受到适当的关注。自然理念是AL是否可用于降低样本注释的成本，同时保留DL的强大学习能力。因此，已经出现了深度主动学习（DAL）。虽然相关的研究非常丰富，但它缺乏对DAL的综合调查。本文要填补这一差距，我们为现有工作提供了正式的分类方法，以及全面和系统的概述。此外，我们还通过申请的角度分析并总结了DAL的发展。最后，我们讨论了DAL中的混乱和问题，为DAL提供了一些可能的发展方向。

我们研究了用于半监控学习（SSL）的无监督数据选择，其中可以提供大规模的未标记数据集，并且为标签采集预算小额数据子集。现有的SSL方法专注于学习一个有效地集成了来自给定小标记数据和大型未标记数据的信息的模型，而我们专注于选择正确的数据以用于SSL的注释，而无需任何标签或任务信息。直观地，要标记的实例应统称为下游任务的最大多样性和覆盖范围，并且单独具有用于SSL的最大信息传播实用程序。我们以三步数据为中心的SSL方法形式化这些概念，使稳定性和精度的纤维液改善8％的CiFar-10（标记为0.08％）和14％的Imagenet -1k（标记为0.2％）。它也是一种具有各种SSL方法的通用框架，提供一致的性能增益。我们的工作表明，在仔细选择注释数据上花费的小计算带来了大注释效率和模型性能增益，而无需改变学习管道。我们完全无监督的数据选择可以轻松扩展到其他弱监督的学习设置。

标记大量数据很昂贵。主动学习旨在通过要求注释未标记的集合中最有用的数据来解决这个问题。我们提出了一种新颖的活跃学习方法，该方法利用自我监督的借口任务和独特的数据采样器来选择既困难又具有代表性的数据。我们发现，简单的自我监督借口任务（例如旋转预测）的损失与下游任务损失密切相关。在主动学习迭代之前，对未标记的集合进行了借口任务学习者进行培训，并且未标记的数据被分类并通过其借口任务损失分组成批处理。在每个主动的学习迭代中，主要任务模型用于批评要注释的批次中最不确定的数据。我们评估了有关各种图像分类和分割基准测试的方法，并在CIFAR10，CALTECH-101，IMAGENET和CITYSCAPES上实现引人注目的性能。我们进一步表明，我们的方法在不平衡的数据集上表现良好，并且可以有效地解决冷启动问题的解决方案，在这种问题中，主动学习性能受到随机采样的初始标记集的影响。

Active learning aims to develop label-efficient algorithms by sampling the most representative queries to be labeled by an oracle. We describe a pool-based semisupervised active learning algorithm that implicitly learns this sampling mechanism in an adversarial manner. Unlike conventional active learning algorithms, our approach is task agnostic, i.e., it does not depend on the performance of the task for which we are trying to acquire labeled data. Our method learns a latent space using a variational autoencoder (VAE) and an adversarial network trained to discriminate between unlabeled and labeled data. The minimax game between the VAE and the adversarial network is played such that while the VAE tries to trick the adversarial network into predicting that all data points are from the labeled pool, the adversarial network learns how to discriminate between dissimilarities in the latent space. We extensively evaluate our method on various image classification and semantic segmentation benchmark datasets and establish a new state of the art on CIFAR10/100, Caltech-256, ImageNet, Cityscapes, and BDD100K. Our results demonstrate that our adversarial approach learns an effective low dimensional latent space in large-scale settings and provides for a computationally efficient sampling method. 1

The generalisation performance of a convolutional neural networks (CNN) is majorly predisposed by the quantity, quality, and diversity of the training images. All the training data needs to be annotated in-hand before, in many real-world applications data is easy to acquire but expensive and time-consuming to label. The goal of the Active learning for the task is to draw most informative samples from the unlabeled pool which can used for training after annotation. With total different objective, self-supervised learning which have been gaining meteoric popularity by closing the gap in performance with supervised methods on large computer vision benchmarks. self-supervised learning (SSL) these days have shown to produce low-level representations that are invariant to distortions of the input sample and can encode invariance to artificially created distortions, e.g. rotation, solarization, cropping etc. self-supervised learning (SSL) approaches rely on simpler and more scalable frameworks for learning. In this paper, we unify these two families of approaches from the angle of active learning using self-supervised learning mainfold and propose Deep Active Learning using BarlowTwins(DALBT), an active learning method for all the datasets using combination of classifier trained along with self-supervised loss framework of Barlow Twins to a setting where the model can encode the invariance of artificially created distortions, e.g. rotation, solarization, cropping etc.

大数据学习为人工智能（AI）带来了成功，但是注释和培训成本很昂贵。将来，对小数据的学习是AI的最终目的之一，它要求机器识别依靠小数据作为人类的目标和场景。一系列的机器学习模型正在进行这种方式，例如积极学习，几乎没有学习，深度聚类。但是，其概括性能几乎没有理论保证。此外，它们的大多数设置都是被动的，也就是说，标签分布由一个指定的采样方案明确控制。这项调查遵循PAC（可能是近似正确）框架下的不可知论活动采样，以分析使用有监督和无监督的时尚对小数据学习的概括误差和标签复杂性。通过这些理论分析，我们从两个几何学角度对小数据学习模型进行了分类：欧几里得和非欧几里得（双曲线）平均表示，在此还提供了优化解决方案和讨论。稍后，然后总结了一些可能从小型数据学习中受益的潜在学习方案，还分析了它们的潜在学习方案。最后，还调查了一些具有挑战性的应用程序，例如计算机视觉，自然语言处理可能会受益于小型数据学习。

我们考虑了一个新颖的表述，即主动射击分类（AFSC）的问题，其目的是对标签预算非常限制的小规定，最初未标记的数据集进行分类。这个问题可以看作是与经典的跨托管少数射击分类（TFSC）的竞争对手范式，因为这两种方法都适用于相似的条件。我们首先提出了一种结合统计推断的方法，以及一种非常适合该框架的原始两级积极学习策略。然后，我们从TFSC领域调整了几个标准视觉基准。我们的实验表明，AFSC的潜在优势可能是很大的，与最先进的TFSC方法相比，对于同一标签预算，平均加权准确性高达10％。我们认为，这种新的范式可能会导致数据筛选学习设置的新发展和标准。

Recent aerial object detection models rely on a large amount of labeled training data, which requires unaffordable manual labeling costs in large aerial scenes with dense objects. Active learning is effective in reducing the data labeling cost by selectively querying the informative and representative unlabelled samples. However, existing active learning methods are mainly with class-balanced setting and image-based querying for generic object detection tasks, which are less applicable to aerial object detection scenario due to the long-tailed class distribution and dense small objects in aerial scenes. In this paper, we propose a novel active learning method for cost-effective aerial object detection. Specifically, both object-level and image-level informativeness are considered in the object selection to refrain from redundant and myopic querying. Besides, an easy-to-use class-balancing criterion is incorporated to favor the minority objects to alleviate the long-tailed class distribution problem in model training. To fully utilize the queried information, we further devise a training loss to mine the latent knowledge in the undiscovered image regions. Extensive experiments are conducted on the DOTA-v1.0 and DOTA-v2.0 benchmarks to validate the effectiveness of the proposed method. The results show that it can save more than 75% of the labeling cost to reach the same performance compared to the baselines and state-of-the-art active object detection methods. Code is available at https://github.com/ZJW700/MUS-CDB

我们在本文中提出了在循环中建立深度神经网络和人类之间的合作，以迅速获得遥感图像的准确分割图。简而言之，代理商迭代地与网络交互以纠正其最初缺陷的预测。具体地，这些相互作用是代表语义标签的注释。我们的方法论贡献是双重的。首先，我们提出了两个交互式学习计划，将用户输入集成到深神经网络中。第一个将注释连接到其他网络的输入。第二个将注释用作稀疏的地面真相来培训网络。其次，我们提出了一种积极的学习策略，以指导用户对诠释的最相关的领域。为此目的，我们比较不同的最先进的获取功能来评估神经网络不确定性，如Confidnet，熵或odin。通过对三个遥感数据集的实验，我们展示了所提出的方法的有效性。值得注意的是，我们表明基于不确定性估计的主动学习使能够快速引导用户对错误而导致错误，因此它与引导用户干预相关联。

人体/手的姿势估计是计算机愿景中的根本问题，基于学习的解决方案需要大量的注释数据。给定有限的注释预算，增加标签效率的常见方法是活动学习（AL），其选择具有最高值的举例，但选择选择策略通常是不变的。在这项工作中，我们改进了在多视图设置中的3D姿态估计问题的主动学习，这在许多应用场景中的重要性越来越重要。我们开发一个框架，使我们能够有效地扩展现有的单视角策略，然后提出两种新的AL策略，可以充分利用多视图几何形状。此外，我们通过纳入预测的伪标签来证明额外的性能提升，这是一种自我训练的形式。我们的系统在三个大型基准测试中显着优于3D身体和手势估计中的基线：CMU Panoptic Studio和Interwand2.6m。值得注意的是，在CMU Panoptic Studio上，我们能够使用仅使用20％的标记培训数据来匹配全监督模型的性能。

机器学习算法中多个超参数的最佳设置是发出大多数可用数据的关键。为此目的，已经提出了几种方法，例如进化策略，随机搜索，贝叶斯优化和启发式拇指规则。在钢筋学习（RL）中，学习代理在与其环境交互时收集的数据的信息内容严重依赖于许多超参数的设置。因此，RL算法的用户必须依赖于基于搜索的优化方法，例如网格搜索或Nelder-Mead单简单算法，这对于大多数R1任务来说是非常效率的，显着减慢学习曲线和离开用户的速度有目的地偏见数据收集的负担。在这项工作中，为了使RL算法更加用户独立，提出了一种使用贝叶斯优化的自主超参数设置的新方法。来自过去剧集和不同的超参数值的数据通过执行行为克隆在元学习水平上使用，这有助于提高最大化获取功能的加强学习变体的有效性。此外，通过紧密地整合在加强学习代理设计中的贝叶斯优化，还减少了收敛到给定任务的最佳策略所需的状态转换的数量。与其他手动调整和基于优化的方法相比，计算实验显示了有希望的结果，这突出了改变算法超级参数来增加所生成数据的信息内容的好处。

We study different aspects of active learning with deep neural networks in a consistent and unified way. i) We investigate incremental and cumulative training modes which specify how the newly labeled data are used for training. ii) We study active learning w.r.t. the model configurations such as the number of epochs and neurons as well as the choice of batch size. iii) We consider in detail the behavior of query strategies and their corresponding informativeness measures and accordingly propose more efficient querying procedures. iv) We perform statistical analyses, e.g., on actively learned classes and test error estimation, that reveal several insights about active learning. v) We investigate how active learning with neural networks can benefit from pseudo-labels as proxies for actual labels.

主动学习在许多领域中展示了数据效率。现有的主动学习算法，特别是在深贝叶斯活动模型的背景下，严重依赖模型的不确定性估计的质量。然而，这种不确定性估计可能会严重偏见，特别是有限和不平衡的培训数据。在本文中，我们建议平衡，贝叶斯深度活跃的学习框架，减轻这种偏差的影响。具体地，平衡采用了一种新的采集功能，该函数利用了等效假设类别捕获的结构，并促进了不同的等价类别之间的分化。直观地，每个等价类包括具有类似预测的深层模型的实例化，并且平衡适应地将等同类的大小调整为学习进展。除了完整顺序设置之外，我们还提出批量平衡 - 顺序算法的泛化算法到批量设置 - 有效地选择批次的培训实施例，这些培训实施例是对模型改进的联合有效的培训实施例。我们展示批量平衡在多个基准数据集上实现了最先进的性能，用于主动学习，并且这两个算法都可以有效地处理通常涉及多级和不平衡数据的逼真挑战。