The use of deep neural networks (DNNs) has recently attracted great attention in the framework of the multi-label classification (MLC) of remote sensing (RS) images. To optimize the large number of parameters of DNNs a high number of reliable training images annotated with multi-labels is often required. However, the collection of a large training set is time-consuming, complex and costly. To minimize annotation efforts for data-demanding DNNs, in this paper we present several query functions for active learning (AL) in the context of DNNs for the MLC of RS images. Unlike the AL query functions defined for single-label classification or semantic segmentation problems, each query function presented in this paper is based on the evaluation of two criteria: i) multi-label uncertainty; and ii) multi-label diversity. The multi-label uncertainty criterion is associated to the confidence of the DNNs in correctly assigning multi-labels to each image. To assess the multi-label uncertainty, we present and adapt to the MLC problems three strategies: i) learning multi-label loss ordering; ii) measuring temporal discrepancy of multi-label prediction; and iii) measuring magnitude of approximated gradient embedding. The multi-label diversity criterion aims at selecting a set of uncertain images that are as diverse as possible to reduce the redundancy among them. To assess this criterion we exploit a clustering based strategy. We combine each of the above-mentioned uncertainty strategy with the clustering based diversity strategy, resulting in three different query functions. Experimental results obtained on two benchmark archives show that our query functions result in the selection of a highly informative set of samples at each iteration of the AL process in the context of MLC.

遥感（RS）图像的多标签分类（MLC）的准确方法的开发是RS中最重要的研究主题之一。基于深度卷积神经网络（CNNS）的方法显示了RS MLC问题的强劲性能。然而，基于CNN的方法通常需要多个陆地覆盖类标签注释的大量可靠的训练图像。收集这些数据是耗时和昂贵的。为了解决这个问题，可包括嘈杂标签的公开专题产品可用于向RS零标记成本注释RS图像。但是，多标签噪声（可能与错误且缺少标签注释相关）可以扭曲MLC算法的学习过程。标签噪声的检测和校正是具有挑战性的任务，尤其是在多标签场景中，其中每个图像可以与多于一个标签相关联。为了解决这个问题，我们提出了一种新的噪声稳健协作多标签学习（RCML）方法，以减轻CNN模型训练期间多标签噪声的不利影响。 RCML在基于三个主模块的RS图像中识别，排名和排除噪声多标签：1）差异模块; 2）组套索模块; 3）交换模块。差异模块确保两个网络了解不同的功能，同时产生相同的预测。组套索模块的任务是检测分配给多标记训练图像的潜在嘈杂的标签，而交换模块任务致力于在两个网络之间交换排名信息。与现有的方法不同，我们提出了关于噪声分布的假设，我们所提出的RCML不会在训练集中的噪声类型之前进行任何先前的假设。我们的代码在线公开提供：http：//www.noisy-labels-in-rs.org

学习遥感（RS）图像之间的相似性形成基于内容的RS图像检索（CBIR）的基础。最近，将图像的语义相似性映射到嵌入（度量标准）空间的深度度量学习方法已经发现非常流行。学习公制空间的常见方法依赖于将与作为锚称为锚的参考图像的类似（正）和不同（负）图像的三胞胎的选择。选择三胞胎是一个难以为多标签RS CBIR的困难任务，其中每个训练图像由多个类标签注释。为了解决这个问题，在本文中，我们提出了一种在为多标签RS CBIR问题定义的深神经网络（DNN）的框架中提出了一种新颖的三联样品采样方法。该方法基于两个主要步骤选择一小部分最多代表性和信息性三元组。在第一步中，使用迭代算法从当前迷你批量选择在嵌入空间中彼此多样化的一组锚。在第二步中，通过基于新颖的策略评估彼此之间的图像的相关性，硬度和多样性来选择不同的正面和负图像。在两个多标签基准档案上获得的实验结果表明，在DNN的上下文中选择最具信息丰富和代表性的三胞胎，导致：i）降低DNN训练阶段的计算复杂性，而性能没有任何显着损失; ii）由于信息性三元组允许快速收敛，因此学习速度的增加。所提出的方法的代码在https://git.tu-berlin.de/rsim/image-reetrieval-from-tropls上公开使用。

The development of deep learning based image representation learning (IRL) methods has attracted great attention in the context of remote sensing (RS) image understanding. Most of these methods require the availability of a high quantity and quality of annotated training images, which can be time-consuming and costly to gather. To reduce labeling costs, publicly available thematic maps, automatic labeling procedures or crowdsourced data can be used. However, such approaches increase the risk of including label noise in training data. It may result in overfitting on noisy labels when discriminative reasoning is employed as in most of the existing methods. This leads to sub-optimal learning procedures, and thus inaccurate characterization of RS images. In this paper, as a first time in RS, we introduce a generative reasoning integrated label noise robust representation learning (GRID) approach. GRID aims to model the complementary characteristics of discriminative and generative reasoning for IRL under noisy labels. To this end, we first integrate generative reasoning into discriminative reasoning through a variational autoencoder. This allows our approach to automatically detect training samples with noisy labels. Then, through our label noise robust hybrid representation learning strategy, GRID adjusts the whole learning procedure for IRL of these samples through generative reasoning and that of the other samples through discriminative reasoning. Our approach learns discriminative image representations while preventing interference of noisy labels during training independently from the IRL method. Thus, unlike the existing methods, GRID does not depend on the type of annotation, label noise, neural network, loss or learning task, and thus can be utilized for various RS image understanding problems. Experimental results show the effectiveness of GRID compared to state-of-the-art methods.

遥感（RS）图像的多标签分类（MLC）精确方法的开发是RS中最重要的研究主题之一。为了解决MLC问题，发现需要大量可靠的可靠训练图像，该图像由多个土地覆盖级标签（多标签）注释，这些培训图像在Rs中很受欢迎。但是，收集这种注释是耗时且昂贵的。以零标签成本获得注释的常见程序是依靠主题产品或众包标签。作为缺点，这些过程具有标签噪声的风险，可能会扭曲MLC算法的学习过程。在文献中，大多数标签噪声鲁棒方法都是针对计算机视觉（CV）中单标签分类（SLC）问题设计的，其中每个图像都由单个标签注释。与SLC不同，MLC中的标签噪声可以与：1）减去标签 - 噪声（在图像中存在该类时，未分配土地覆盖类标签为图像）； 2）添加标签噪声（尽管该类不存在在给定图像中，但将土地覆盖类标签分配给图像）； 3）混合标签 - 噪声（两者的组合）。在本文中，我们研究了三种不同的噪声鲁棒CV SLC方法，并将其适应为RS的多标签噪声场景。在实验过程中，我们研究了不同类型的多标签噪声的影响，并严格评估了适用的方法。为此，我们还引入了一种合成的多标签噪声注入策略，该策略与统一标签噪声注入策略相比，该策略更适合模拟操作场景，在该策略中，缺少和当前类的标签以均匀的概率上翻转。此外，我们研究了噪声多标签下不同评估指标在MLC问题中的相关性。

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.

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.

主动学习（AL）是应选择的数据用于注释。现有的工作试图选择高度不确定或信息性的注释数据。尽管如此，它仍然不清楚所选择的数据如何影响AL中使用的任务模型的测试性能。在这项工作中，我们通过理论上证明，选择更高梯度规范的未标记数据导致测试损失的较低的上限，从而探讨了这种影响，从而产生更好的测试性能。但是，由于缺乏标签信息，直接计算未标记数据的梯度标准是不可行的。为了解决这一挑战，我们提出了两种计划，即预期的Gradnorm和熵 - Gradnorm。前者通过构建预期的经验损失来计算梯度规范，而后者用熵构造无监督的损失。此外，我们将这两个方案集成在通用AL框架中。我们在古典图像分类和语义分割任务中评估我们的方法。为了展示其域应用程序的能力及其对噪声的鲁棒性，我们还在蜂窝成像分析任务中验证了我们的方法，即Cryo-Collecton Subtom图分类。结果表明，我们的方法达到了最先进的卓越性能。我们的源代码可在https://github.com/xulabs/aitom提供

大型标记数据集的可用性是深度学习成功的关键组成部分。但是，大型数据集上的标签通常很耗时且昂贵。主动学习是一个研究领域，通过选择最重要的标签样本来解决昂贵的标签问题。基于多样性的采样算法被称为基于表示的主动学习方法的组成部分。在本文中，我们介绍了一种新的基于多样性的初始数据集选择算法，以选择有效学习环境中初始标记的最有用的样本集。自我监督的表示学习用于考虑初始数据集选择算法中样品的多样性。此外，我们提出了一种新型的主动学习查询策略，该策略使用基于多样性的基于一致性的嵌入方式采样。通过考虑基于一致性的嵌入方案中多样性的一致性信息，该方法可以在半监督的学习环境中选择更多信息的样本来标记。比较实验表明，通过利用未标记的数据的多样性，与先前的主动学习方法相比，该提出的方法在CIFAR-10和CALTECH-101数据集上取得了令人信服的结果。

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.

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

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。

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.

虽然深度学习（DL）是渴望数据的，并且通常依靠广泛的标记数据来提供良好的性能，但主动学习（AL）通过从未标记的数据中选择一小部分样本进行标签和培训来降低标签成本。因此，近年来，在有限的标签成本/预算下，深入的积极学习（DAL）是可行的解决方案，可在有限的标签成本/预算下最大化模型性能。尽管已经开发了大量的DAL方法并进行了各种文献综述，但在公平比较设置下对DAL方法的性能评估尚未可用。我们的工作打算填补这一空白。在这项工作中，我们通过重新实现19种引用的DAL方法来构建DAL Toolkit，即Deepal+。我们调查和分类与DAL相关的作品，并构建经常使用的数据集和DAL算法的比较实验。此外，我们探讨了影响DAL功效的一些因素（例如，批处理大小，训练过程中的时期数），这些因素为研究人员设计其DAL实验或执行DAL相关应用程序提供了更好的参考。

命名实体识别（ner）旨在标识在非结构化文本中的命名实体的提到，并将它们分类为预定义的命名实体类。尽管基于深度学习的预先训练的语言模型实现了良好的预测性能，但许多域特定的NERTASK仍然需要足够量的标记数据。主动学习（AL）是标签采集问题的一般框架，已用于NER任务，以最大限度地降低注释成本而不会牺牲模型性能。然而，令牌的严重不平衡的课程分布引入了设计有效的NER Querying方法的挑战。我们提出了al句子查询评估函数，这些函数更加关注可能的积极令牌，并评估基于句子和基于令牌的成本评估策略的这些提出的功能。我们还提出了更好的数据驱动的归一化方法来惩罚太长或太短的句子。我们在来自不同域的三个数据集上的实验表明，所提出的方法减少了带有常规方法的更好或可比预测性能的增注令牌的数量。

The performance of deep neural networks improves with more annotated data. The problem is that the budget for annotation is limited. One solution to this is active learning, where a model asks human to annotate data that it perceived as uncertain. A variety of recent methods have been proposed to apply active learning to deep networks but most of them are either designed specific for their target tasks or computationally inefficient for large networks. In this paper, we propose a novel active learning method that is simple but task-agnostic, and works efficiently with the deep networks. We attach a small parametric module, named "loss prediction module," to a target network, and learn it to predict target losses of unlabeled inputs. Then, this module can suggest data that the target model is likely to produce a wrong prediction. This method is task-agnostic as networks are learned from a single loss regardless of target tasks. We rigorously validate our method through image classification, object detection, and human pose estimation, with the recent network architectures. The results demonstrate that our method consistently outperforms the previous methods over the tasks.

我们介绍了有监督的对比度积极学习（SCAL），并根据功能相似性（功能IM）和基于主成分分析的基于特征重建误差（FRE）提出有效的活动学习策略，以选择具有不同特征表示的信息性数据示例。我们证明了我们提出的方法可实现最新的准确性，模型校准并减少在图像分类任务上平衡和不平衡数据集的主动学习设置中的采样偏差。我们还评估了模型的鲁棒性，从主动学习环境中不同查询策略得出的分配转移。使用广泛的实验，我们表明我们提出的方法的表现优于高性能密集型方法，从而使平均损坏误差降低了9.9％，在数据集偏移下的预期校准误差降低了7.2％，而AUROC降低了8.9％的AUROC。检测。

哥内克人Sentinel Imagery的纯粹卷的可用性为使用深度学习的大尺度创造了新的土地利用陆地覆盖（Lulc）映射的机会。虽然在这种大型数据集上培训是一个非琐碎的任务。在这项工作中，我们试验Lulc Image分类和基准不同最先进模型的Bigearthnet数据集，包括卷积神经网络，多层感知，视觉变压器，高效导通和宽残余网络（WRN）架构。我们的目标是利用分类准确性，培训时间和推理率。我们提出了一种基于用于网络深度，宽度和输入数据分辨率的WRNS复合缩放的高效导通的框架，以有效地训练和测试不同的模型设置。我们设计一种新颖的缩放WRN架构，增强了有效的通道注意力机制。我们提出的轻量级模型具有较小的培训参数，实现所有19个LULC类的平均F分类准确度达到4.5％，并且验证了我们使用的resnet50最先进的模型速度快两倍作为基线。我们提供超过50种培训的型号，以及我们在多个GPU节点上分布式培训的代码。

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。

尽管深入学习对监督点云语义细分的成功取得了成功，但获得大规模的逐点手动注释仍然是一个重大挑战。为了减轻巨大的注释负担，我们提出了一个基于区域和多样性的积极学习（REDAL），这是许多深度学习方法的一般框架，旨在自动选择用于标签获取的信息丰富和多样化的子场所。观察到只有一小部分带注释的区域足以通过深度学习的方式理解3D场景，我们使用SoftMax熵，颜色不连续性和结构复杂性来衡量子场所区域的信息。还开发了一种多样性的选择算法，以避免通过在查询批次中选择信息性但相似的区域而产生的多余注释。广泛的实验表明，我们的方法的表现高于先前的活跃学习策略，并且我们达到了90％的全面监督学习，而S3DIS和Semantickitti数据集则需要不到15％和5％的注释。我们的代码可在https://github.com/tsunghan-wu/redal上公开获取。