典型的多源域适应性（MSDA）方法旨在将知识从一组标记的源域中学习的知识转移到一个未标记的目标域。然而，先前的工作严格假设每个源域都与目标域共享相同的类别类别，因为目标标签空间无法观察到，这几乎无法保证。在本文中，我们考虑了MSDA的更广泛的设置，即广义的多源域适应性，其中源域部分重叠，并且允许目标域包含任何源域中未呈现的新型类别。由于域的共存和类别跨源域和目标域的转移，因此这种新设置比任何现有的域适应协议都难以捉摸。为了解决这个问题，我们提出了一个变分域分解（VDD）框架，该框架通过鼓励尺寸独立性来分解每个实例的域表示和语义特征。为了识别未知类别的目标样本，我们利用在线伪标签，该标签将伪标签分配给基于置信分数的未标记目标数据。在两个基准数据集上进行的定量和定性实验证明了拟议框架的有效性。

虽然在许多域内生成并提供了大量的未标记数据，但对视觉数据的自动理解的需求高于以往任何时候。大多数现有机器学习模型通常依赖于大量标记的训练数据来实现高性能。不幸的是，在现实世界的应用中，不能满足这种要求。标签的数量有限，手动注释数据昂贵且耗时。通常需要将知识从现有标记域传输到新域。但是，模型性能因域之间的差异（域移位或数据集偏差）而劣化。为了克服注释的负担，域适应（DA）旨在在将知识从一个域转移到另一个类似但不同的域中时减轻域移位问题。无监督的DA（UDA）处理标记的源域和未标记的目标域。 UDA的主要目标是减少标记的源数据和未标记的目标数据之间的域差异，并在培训期间在两个域中学习域不变的表示。在本文中，我们首先定义UDA问题。其次，我们从传统方法和基于深度学习的方法中概述了不同类别的UDA的最先进的方法。最后，我们收集常用的基准数据集和UDA最先进方法的报告结果对视觉识别问题。

The cost of large scale data collection and annotation often makes the application of machine learning algorithms to new tasks or datasets prohibitively expensive. One approach circumventing this cost is training models on synthetic data where annotations are provided automatically. Despite their appeal, such models often fail to generalize from synthetic to real images, necessitating domain adaptation algorithms to manipulate these models before they can be successfully applied. Existing approaches focus either on mapping representations from one domain to the other, or on learning to extract features that are invariant to the domain from which they were extracted. However, by focusing only on creating a mapping or shared representation between the two domains, they ignore the individual characteristics of each domain. We suggest that explicitly modeling what is unique to each domain can improve a model's ability to extract domain-invariant features. Inspired by work on private-shared component analysis, we explicitly learn to extract image representations that are partitioned into two subspaces: one component which is private to each domain and one which is shared across domains. Our model is trained not only to perform the task we care about in the source domain, but also to use the partitioned representation to reconstruct the images from both domains. Our novel architecture results in a model that outperforms the state-of-the-art on a range of unsupervised domain adaptation scenarios and additionally produces visualizations of the private and shared representations enabling interpretation of the domain adaptation process.

大多数现有的多源域适配（MSDA）方法通过特征分布对准最小化多个源 - 目标域对之间的距离，从单个源设置借用的方法。但是，对于不同的源极域，对齐成对特征分布是具有挑战性的，甚至可以对MSDA进行反效率。在本文中，我们介绍了一种新颖的方法：可转让的属性学习。动机很简单：虽然不同的域可以具有急剧不同的视野，但它们包含相同的类类，其特征在一起相同的属性;因此，MSDA模型应该专注于学习目标域的最可转换的属性。采用这种方法，我们提出了域名关注一致性网络，称为DAC网。关键设计是一个特征通道注意模块，旨在识别可转移功能（属性）。重要的是，注意模块受到一致性损失的监督，这对源极和目标域之间的信道注意权重的分布施加。此外，为了促进对目标数据的鉴别特征学习，我们将伪标记与类紧凑性丢失相结合，以最小化目标特征和分类器的权重向量之间的距离。在三个MSDA基准测试中进行了广泛的实验表明，我们的DAC-NET在所有这些中实现了新的最新性能。

Deep domain adaptation has emerged as a new learning technique to address the lack of massive amounts of labeled data. Compared to conventional methods, which learn shared feature subspaces or reuse important source instances with shallow representations, deep domain adaptation methods leverage deep networks to learn more transferable representations by embedding domain adaptation in the pipeline of deep learning. There have been comprehensive surveys for shallow domain adaptation, but few timely reviews the emerging deep learning based methods. In this paper, we provide a comprehensive survey of deep domain adaptation methods for computer vision applications with four major contributions. First, we present a taxonomy of different deep domain adaptation scenarios according to the properties of data that define how two domains are diverged. Second, we summarize deep domain adaptation approaches into several categories based on training loss, and analyze and compare briefly the state-of-the-art methods under these categories. Third, we overview the computer vision applications that go beyond image classification, such as face recognition, semantic segmentation and object detection. Fourth, some potential deficiencies of current methods and several future directions are highlighted.

深度神经网络已经证明了他们可以从数据中自动提取有意义的功能的能力。但是，在监督学习中，特定于用于培训的数据集的信息，但与手头的任务无关，可以在提取的表示中仍然被编码。该剩余信息引入了特定于域的偏差，削弱了泛化性能。在这项工作中，我们建议将信息分成与任务相关的表示及其互补情境表示。我们提出了一种原始方法，结合对抗特征预测器和循环重建，以解开单域监督案例中的这两个表示。然后，我们将该方法适应无监督的域适应问题，包括训练能够在源域和目标域上执行的模型。特别是，尽管没有训练标签，我们的方法促进了目标领域的解剖学。这使得能够将特定于域的特定任务信息隔离为公共表示。任务特定的表示允许有效地将从源域获取的知识转移到目标域。在单一域案中，我们展示了我们关于信息检索任务的陈述的质量以及由锐化的任务特定陈述引起的泛化效益。然后，我们在几个古典域适应基准上验证所提出的方法，并说明了解除域适应的益处。

In this paper, we investigate a challenging unsupervised domain adaptation setting -unsupervised model adaptation. We aim to explore how to rely only on unlabeled target data to improve performance of an existing source prediction model on the target domain, since labeled source data may not be available in some real-world scenarios due to data privacy issues. For this purpose, we propose a new framework, which is referred to as collaborative class conditional generative adversarial net to bypass the dependence on the source data. Specifically, the prediction model is to be improved through generated target-style data, which provides more accurate guidance for the generator. As a result, the generator and the prediction model can collaborate with each other without source data. Furthermore, due to the lack of supervision from source data, we propose a weight constraint that encourages similarity to the source model. A clustering-based regularization is also introduced to produce more discriminative features in the target domain. Compared to conventional domain adaptation methods, our model achieves superior performance on multiple adaptation tasks with only unlabeled target data, which verifies its effectiveness in this challenging setting.

Recent works on domain adaptation reveal the effectiveness of adversarial learning on filling the discrepancy between source and target domains. However, two common limitations exist in current adversarial-learning-based methods. First, samples from two domains alone are not sufficient to ensure domain-invariance at most part of latent space. Second, the domain discriminator involved in these methods can only judge real or fake with the guidance of hard label, while it is more reasonable to use soft scores to evaluate the generated images or features, i.e., to fully utilize the inter-domain information. In this paper, we present adversarial domain adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a more continuous latent space and guides the domain discriminator in judging samples' difference relative to source and target domains. Domain mixup is jointly conducted on pixel and feature level to improve the robustness of models. Extensive experiments prove that the proposed approach can achieve superior performance on tasks with various degrees of domain shift and data complexity.

无监督的视频域适应是一项实用但具有挑战性的任务。在这项工作中，我们第一次从脱离视图中解决了它。我们的关键想法是在适应过程中将与域相关的信息从数据中删除。具体而言，我们考虑从两组潜在因素中生成跨域视频，一个编码静态域相关信息，另一个编码时间和语义相关的信息。然后开发转移顺序的VAE（Transvae）框架以建模这种产生。为了更好地适应适应，我们进一步提出了几个目标，以限制Transvae中的潜在因素。与几种最先进的方法相比，对UCF-HMDB，小丑和Epic-Kitchens数据集进行了广泛的实验验证了Transvae的有效性和优势。代码可在https://github.com/ldkong1205/transvae上公开获取。

无监督域适应（UDA）旨在将知识从相关但不同的良好标记的源域转移到新的未标记的目标域。大多数现有的UDA方法需要访问源数据，因此当数据保密而不相配在隐私问题时，不适用。本文旨在仅使用培训的分类模型来解决现实设置，而不是访问源数据。为了有效地利用适应源模型，我们提出了一种新颖的方法，称为源假设转移（拍摄），其通过将目标数据特征拟合到冻结源分类模块（表示分类假设）来学习目标域的特征提取模块。具体而言，拍摄挖掘出于特征提取模块的信息最大化和自我监督学习，以确保目标特征通过同一假设与看不见的源数据的特征隐式对齐。此外，我们提出了一种新的标签转移策略，它基于预测的置信度（标签信息），然后采用半监督学习来将目标数据分成两个分裂，然后提高目标域中的较为自信预测的准确性。如果通过拍摄获得预测，我们表示标记转移为拍摄++。关于两位数分类和对象识别任务的广泛实验表明，拍摄和射击++实现了与最先进的结果超越或相当的结果，展示了我们对各种视域适应问题的方法的有效性。代码可用于\ url {https：//github.com/tim-learn/shot-plus}。

关于无监督域适应性（UDA）的大多数现有研究都认为每个域的训练样本都带有域标签（例如绘画，照片）。假定每个域中的样品都遵循相同的分布，并利用域标签通过特征对齐来学习域不变特征。但是，这样的假设通常并不成立 - 通常存在许多较细粒的领域（例如，已经开发出了数十种现代绘画样式，每种绘画样式与经典风格的范围都有很大不同）。因此，在每个人工定义和粗粒结构域之间强迫特征分布对齐可能是无效的。在本文中，我们从完全不同的角度解决了单源和多源UDA，即将每个实例视为一个良好的域。因此，跨域的特征对齐是冗余。相反，我们建议执行动态实例域的适应性（DIDA）。具体而言，开发了具有自适应卷积内核的动态神经网络，以生成实例自适应残差，以使域 - 无知的深度特征适应每个单独的实例。这使得共享分类器可以同时应用于源域数据，而无需依赖任何域注释。此外，我们没有施加复杂的特征对准损失，而是仅使用标记的源和伪标记为目标数据的跨透镜损失采用简单的半监督学习范式。我们的模型被称为DIDA-NET，可以在几种常用的单源和多源UDA数据集上实现最先进的性能，包括数字，办公室房屋，域名，域名，Digit-Five和PAC。

Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA methods typically require to access the source data when learning to adapt the model, making them risky and inefficient for decentralized private data. This work tackles a practical setting where only a trained source model is available and investigates how we can effectively utilize such a model without source data to solve UDA problems. We propose a simple yet generic representation learning framework, named Source HypOthesis Transfer (SHOT). SHOT freezes the classifier module (hypothesis) of the source model and learns the target-specific feature extraction module by exploiting both information maximization and selfsupervised pseudo-labeling to implicitly align representations from the target domains to the source hypothesis. To verify its versatility, we evaluate SHOT in a variety of adaptation cases including closed-set, partial-set, and open-set domain adaptation. Experiments indicate that SHOT yields state-of-the-art results among multiple domain adaptation benchmarks.

多源域适应（MSDA）着重于将知识从多个源域转移到目标域，这与常规的单源域适应性相比，这是一个更实用和具有挑战性的问题。在此问题中，必须共同对多个源域和目标域进行建模，并且也需要有效的域组合方案。不同领域之间的图形结构对于应对这些挑战很有用，在这些挑战中，可以有效地对各种实例/类别之间的相互依赖性进行建模。在这项工作中，我们提出了两种类型的图形模型，即MSDA（CRF-MSDA）的条件随机场和MSDA的Markov随机场（MRF-MSDA），用于跨域关节建模和可学习的域组合。简而言之，给定一个由查询样品和语义原型（即代表性类别嵌入）组成的观察集，CRF-MSDA模型旨在学习根据观测值调节标签的联合分布。我们通过在所有观察结果上构建一个关系图并进行当地消息来实现这一目标。相比之下，MRF-MSDA旨在通过基于能量的公式对观测值的联合分布进行建模，并且它可以通过求和几个特定网络的联合可能性来自然执行标签预测。与CRF-MSDA对应物相比，MRF-MSDA模型具有更高的表达性，并且具有较低的计算成本。我们在具有独特的域移位和数据复杂性的四个标准基准数据集上评估了这两个模型，并且两个模型都在所有基准测试基准上都具有优于现有方法的性能。此外，分析研究说明了不同模型成分的效果，并提供了有关跨域关节建模如何执行的见解。

We propose a general framework for unsupervised domain adaptation, which allows deep neural networks trained on a source domain to be tested on a different target domain without requiring any training annotations in the target domain. This is achieved by adding extra networks and losses that help regularize the features extracted by the backbone encoder network. To this end we propose the novel use of the recently proposed unpaired image-toimage translation framework to constrain the features extracted by the encoder network. Specifically, we require that the features extracted are able to reconstruct the images in both domains. In addition we require that the distribution of features extracted from images in the two domains are indistinguishable. Many recent works can be seen as specific cases of our general framework. We apply our method for domain adaptation between MNIST, USPS, and SVHN datasets, and Amazon, Webcam and DSLR Office datasets in classification tasks, and also between GTA5 and Cityscapes datasets for a segmentation task. We demonstrate state of the art performance on each of these datasets.

Conventional unsupervised domain adaptation (UDA) assumes that training data are sampled from a single domain. This neglects the more practical scenario where training data are collected from multiple sources, requiring multi-source domain adaptation. We make three major contributions towards addressing this problem. First, we collect and annotate by far the largest UDA dataset, called DomainNet, which contains six domains and about 0.6 million images distributed among 345 categories, addressing the gap in data availability for multi-source UDA research. Second, we propose a new deep learning approach, Moment Matching for Multi-Source Domain Adaptation (M 3 SDA), which aims to transfer knowledge learned from multiple labeled source domains to an unlabeled target domain by dynamically aligning moments of their feature distributions. Third, we provide new theoretical insights specifically for moment matching approaches in both single and multiple source domain adaptation. Extensive experiments are conducted to demonstrate the power of our new dataset in benchmarking state-of-the-art multi-source domain adaptation methods, as well as the advantage of our proposed model. Dataset and Code are available at http://ai.bu.edu/M3SDA/

Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn from the same distribution, which may not always be the case. As a complement to this challenge, single-source unsupervised domain adaptation can handle situations where a network is trained on labeled data from a source domain and unlabeled data from a related but different target domain with the goal of performing well at test-time on the target domain. Many single-source and typically homogeneous unsupervised deep domain adaptation approaches have thus been developed, combining the powerful, hierarchical representations from deep learning with domain adaptation to reduce reliance on potentially-costly target data labels. This survey will compare these approaches by examining alternative methods, the unique and common elements, results, and theoretical insights. We follow this with a look at application areas and open research directions.

虽然无监督的域适应（UDA）算法，即，近年来只有来自源域的标记数据，大多数算法和理论结果侧重于单源无监督域适应（SUDA）。然而，在实际情况下，标记的数据通常可以从多个不同的源收集，并且它们可能不仅不同于目标域而且彼此不同。因此，来自多个源的域适配器不应以相同的方式进行建模。最近基于深度学习的多源无监督域适应（Muda）算法专注于通过在通用特征空间中的所有源极和目标域的分布对齐来提取所有域的公共域不变表示。但是，往往很难提取Muda中所有域的相同域不变表示。此外，这些方法匹配分布而不考虑类之间的域特定的决策边界。为了解决这些问题，我们提出了一个新的框架，具有两个对准阶段的Muda，它不仅将每对源和目标域的分布对齐，而且还通过利用域特定的分类器的输出对准决策边界。广泛的实验表明，我们的方法可以对图像分类的流行基准数据集实现显着的结果。

无监督的域适应方法最近在各种医学图像分割任务中成功了。报告的作品通常通过对齐域不变特征并最大程度地减少特定于域的差异来解决域移位问题。当特定域之间的差异和不同域之间的差异很小时，该策略效果很好。但是，这些模型对各种成像方式的概括能力仍然是一个重大挑战。本文介绍了UDA-VAE ++，这是一种无监督的域适应框架，用于心脏分割，并具有紧凑的损失函数下限。为了估算这一新的下限，我们使用全局估计器，局部估计器和先前的信息匹配估计器开发了新的结构共同信息估计（SMIE）块，以最大程度地提高重建和分割任务之间的相互信息。具体而言，我们设计了一种新型的顺序重新聚集方案，该方案可以实现从低分辨率潜在空间到高分辨率潜在空间的信息流和方差校正。基准心脏分割数据集的全面实验表明，我们的模型在定性和定量上优于先前的最先进。该代码可在https://github.com/louey233/toward-mutual-information} {https://github.com/louey233/toward-mutual-information中获得

自我监督的学习（SSL）最近成为特征学习方法中的最爱。因此，它可以吸引域适应方法来考虑结合SSL。直觉是强制执行实例级别一致性，使得预测器在域中变得不变。但是，域适应制度中的大多数现有SSL方法通常被视为独立的辅助组件，使域自适应的签名无人看管。实际上，域间隙消失的最佳区域和SSL PERUSES的实例级别约束可能根本不一致。从这一点来看，我们向一个特定的范式的自我监督学习量身定制，用于域适应，即可转让的对比学习（TCL），这与SSL和所需的跨域转移性相一致地联系起来。我们发现对比学习本质上是一个合适的域适应候选者，因为它的实例不变性假设可以方便地促进由域适应任务青睐的跨域类级不变性。基于特定的记忆库结构和伪标签策略，TCL然后通过清洁和新的对比损失来惩罚源头和靶之间的跨域内域差异。免费午餐是由于纳入对比学习，TCL依赖于移动平均的关键编码器，自然地实现了用于目标数据的伪标签的暂停标签，这避免了无额外的成本。因此，TCL有效地减少了跨域间隙。通过对基准（Office-Home，Visda-2017，Diamet-Five，PACS和Domainnet）进行广泛的实验，用于单源和多源域适配任务，TCL已经证明了最先进的性能。

通过从完全标记的源域中利用数据，无监督域适应（UDA）通过显式差异最小化数据分布或对抗学习来提高未标记的目标域上的分类性能。作为增强，通过利用模型预测来加强目标特征识别期间涉及类别对齐。但是，在目标域上的错误类别预测中产生的伪标签不准确以及由源域的过度录制引起的分发偏差存在未探明的问题。在本文中，我们提出了一种模型 - 不可知的两阶段学习框架，这大大减少了使用软伪标签策略的缺陷模型预测，并避免了课程学习策略的源域上的过度拟合。从理论上讲，它成功降低了目标域上预期误差的上限的综合风险。在第一阶段，我们用分布对齐的UDA方法训练一个模型，以获得具有相当高的置位目标域上的软语义标签。为了避免在源域上的过度拟合，在第二阶段，我们提出了一种课程学习策略，以自适应地控制来自两个域的损失之间的加权，以便训练阶段的焦点从源分布逐渐移位到目标分布，以预测信心提升了目标分布在目标领域。对两个知名基准数据集的广泛实验验证了我们提出框架促进促进顶级UDA算法的性能的普遍效果，并展示其一致的卓越性能。