We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains.The approach implements this idea in the context of neural network architectures that are trained on labeled data from the source domain and unlabeled data from the target domain (no labeled target-domain data is necessary). As the training progresses, the approach promotes the emergence of features that are (i) discriminative for the main learning task on the source domain and (ii) indiscriminate with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation and stochastic gradient descent, and can thus be implemented with little effort using any of the deep learning packages.We demonstrate the success of our approach for two distinct classification problems (document sentiment analysis and image classification), where state-of-the-art domain adaptation performance on standard benchmarks is achieved. We also validate the approach for descriptor learning task in the context of person re-identification application.
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Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available. Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled targetdomain data is necessary).As the training progresses, the approach promotes the emergence of "deep" features that are (i) discriminative for the main learning task on the source domain and (ii) invariant with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a simple new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation.Overall, the approach can be implemented with little effort using any of the deep-learning packages. The method performs very well in a series of image classification experiments, achieving adaptation effect in the presence of big domain shifts and outperforming previous state-ofthe-art on Office datasets.
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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.
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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.
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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。
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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.
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Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation Network (DAN) architecture, which generalizes deep convolutional neural network to the domain adaptation scenario. In DAN, hidden representations of all task-specific layers are embedded in a reproducing kernel Hilbert space where the mean embeddings of different domain distributions can be explicitly matched. The domain discrepancy is further reduced using an optimal multi-kernel selection method for mean embedding matching. DAN can learn transferable features with statistical guarantees, and can scale linearly by unbiased estimate of kernel embedding. Extensive empirical evidence shows that the proposed architecture yields state-of-the-art image classification error rates on standard domain adaptation benchmarks.
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Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. Due to the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning researches, as well as to summarize and interpret the mechanisms and the strategies of transfer learning in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Unlike previous surveys, this survey paper reviews more than forty representative transfer learning approaches, especially homogeneous transfer learning approaches, from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, over twenty representative transfer learning models are used for experiments. The models are performed on three different datasets, i.e., Amazon Reviews, Reuters-21578, and Office-31. And the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.
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In this work, we present a method for unsupervised domain adaptation. Many adversarial learning methods train domain classifier networks to distinguish the features as either a source or target and train a feature generator network to mimic the discriminator. Two problems exist with these methods. First, the domain classifier only tries to distinguish the features as a source or target and thus does not consider task-specific decision boundaries between classes. Therefore, a trained generator can generate ambiguous features near class boundaries. Second, these methods aim to completely match the feature distributions between different domains, which is difficult because of each domain's characteristics.To solve these problems, we introduce a new approach that attempts to align distributions of source and target by utilizing the task-specific decision boundaries. We propose to maximize the discrepancy between two classifiers' outputs to detect target samples that are far from the support of the source. A feature generator learns to generate target features near the support to minimize the discrepancy. Our method outperforms other methods on several datasets of image classification and semantic segmentation. The codes are available at https://github. com/mil-tokyo/MCD_DA
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Domain adaptation aims at generalizing a high-performance learner on a target domain via utilizing the knowledge distilled from a source domain which has a different but related data distribution. One solution to domain adaptation is to learn domain invariant feature representations while the learned representations should also be discriminative in prediction. To learn such representations, domain adaptation frameworks usually include a domain invariant representation learning approach to measure and reduce the domain discrepancy, as well as a discriminator for classification. Inspired by Wasserstein GAN, in this paper we propose a novel approach to learn domain invariant feature representations, namely Wasserstein Distance Guided Representation Learning (WD-GRL). WDGRL utilizes a neural network, denoted by the domain critic, to estimate empirical Wasserstein distance between the source and target samples and optimizes the feature extractor network to minimize the estimated Wasserstein distance in an adversarial manner. The theoretical advantages of Wasserstein distance for domain adaptation lie in its gradient property and promising generalization bound. Empirical studies on common sentiment and image classification adaptation datasets demonstrate that our proposed WDGRL outperforms the state-of-the-art domain invariant representation learning approaches.
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无监督域适应(UDA)旨在将知识从标记的源域传输到未标记的目标域。传统上,基于子空间的方法为此问题形成了一类重要的解决方案。尽管他们的数学优雅和易腐烂性,但这些方法通常被发现在产生具有复杂的现实世界数据集的领域不变的功能时无效。由于近期具有深度网络的代表学习的最新进展,本文重新访问了UDA的子空间对齐,提出了一种新的适应算法,始终如一地导致改进的泛化。与现有的基于对抗培训的DA方法相比,我们的方法隔离了特征学习和分配对准步骤,并利用主要辅助优化策略来有效地平衡域不契约的目标和模型保真度。在提供目标数据和计算要求的显着降低的同时,基于子空间的DA竞争性,有时甚至优于几种标准UDA基准测试的最先进的方法。此外,子空间对准导致本质上定期的模型,即使在具有挑战性的部分DA设置中,也表现出强大的泛化。最后,我们的UDA框架的设计本身支持对测试时间的新目标域的逐步适应,而无需从头开始重新检测模型。总之,由强大的特征学习者和有效的优化策略提供支持,我们将基于子空间的DA建立为可视识别的高效方法。
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深度学习(DL)是各种计算机视觉任务中使用的主要方法,因为它在许多任务上取得了相关结果。但是,在具有部分或没有标记数据的实际情况下,DL方法也容易出现众所周知的域移位问题。多源无监督的域适应性(MSDA)旨在通过从一袋源模型中分配弱知识来学习未标记域的预测指标。但是,大多数作品进行域适应性仅利用提取的特征并从损失函数设计的角度降低其域的转移。在本文中,我们认为仅基于域级特征处理域移动不足,但是在功能空间上对此类信息进行对齐也是必不可少的。与以前的工作不同,我们专注于网络设计,并建议将多源版本的域对齐层(MS-DIAL)嵌入预测变量的不同级别。这些层旨在匹配不同域之间的特征分布,并且可以轻松地应用于各种MSDA方法。为了显示我们方法的鲁棒性,我们考虑了两个具有挑战性的情况:数字识别和对象分类,进行了广泛的实验评估。实验结果表明,我们的方法可以改善最新的MSDA方法,从而在其分类精度上获得 +30.64%的相对增长。
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半监督域适应(SSDA)是一种具有挑战性的问题,需要克服1)以朝向域的较差的数据和2)分布换档的方法。不幸的是,由于培训数据偏差朝标标样本训练,域适应(DA)和半监督学习(SSL)方法的简单组合通常无法解决这两个目的。在本文中,我们介绍了一种自适应结构学习方法,以规范SSL和DA的合作。灵感来自多视图学习,我们建议的框架由共享特征编码器网络和两个分类器网络组成,用于涉及矛盾的目的。其中,其中一个分类器被应用于组目标特征以提高级别的密度,扩大了鲁棒代表学习的分类集群的间隙。同时,其他分类器作为符号器,试图散射源功能以增强决策边界的平滑度。目标聚类和源扩展的迭代使目标特征成为相应源点的扩张边界内的封闭良好。对于跨域特征对齐和部分标记的数据学习的联合地址,我们应用最大平均差异(MMD)距离最小化和自培训(ST)将矛盾结构投影成共享视图以进行可靠的最终决定。对标准SSDA基准的实验结果包括Domainnet和Office-Home,展示了我们对最先进的方法的方法的准确性和稳健性。
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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}。
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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。
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Domain Adaptation is an actively researched problem in Computer Vision. In this work, we propose an approach that leverages unsupervised data to bring the source and target distributions closer in a learned joint feature space. We accomplish this by inducing a symbiotic relationship between the learned embedding and a generative adversarial network. This is in contrast to methods which use the adversarial framework for realistic data generation and retraining deep models with such data. We demonstrate the strength and generality of our approach by performing experiments on three different tasks with varying levels of difficulty: (1) Digit classification (MNIST, SVHN and USPS datasets) (2) Object recognition using OFFICE dataset and (3) Domain adaptation from synthetic to real data. Our method achieves state-of-the art performance in most experimental settings and by far the only GAN-based method that has been shown to work well across different datasets such as OFFICE and DIGITS.
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域适应性是现代机器学习中的一种流行范式,旨在解决培训或验证数据集之间具有用于学习和测试分类器(源域)和潜在的大型未标记数据集的培训或验证数据集之间的分歧问题,其中利用了模型(目标域)(目标域)(目标域) 。任务是找到源数据集的源和目标数据集的这种常见表示,其中源数据集提供了培训的信息,因此可以最大程度地减少来源和目标之间的差异。目前,最流行的领域适应性解决方案是基于训练神经网络,这些神经网络结合了分类和对抗性学习模块,这些模块是饥饿的,通常很难训练。我们提出了一种称为域适应性主成分分析(DAPCA)的方法,该方法发现线性减少的数据表示有助于解决域适应任务。 DAPCA基于数据点对之间引入正权重,并概括了主成分分析的监督扩展。 DAPCA代表一种迭代算法,因此在每次迭代中都解决了一个简单的二次优化问题。保证算法的收敛性,并且在实践中的迭代次数很少。我们验证了先前提出的用于解决域适应任务的基准的建议算法,还显示了在生物医学应用中对单细胞法数据集进行分析中使用DAPCA的好处。总体而言,考虑到源域和目标域之间可能的差异,DAPCA可以作为许多机器学习应用程序中有用的预处理步骤。
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Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias. Fine-tuning deep models in a new domain can require a significant amount of labeled data, which for many applications is simply not available. We propose a new CNN architecture to exploit unlabeled and sparsely labeled target domain data. Our approach simultaneously optimizes for domain invariance to facilitate domain transfer and uses a soft label distribution matching loss to transfer information between tasks. Our proposed adaptation method offers empirical performance which exceeds previously published results on two standard benchmark visual domain adaptation tasks, evaluated across supervised and semi-supervised adaptation settings.
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在计算机视觉中,面对域转移是很常见的:具有相同类但采集条件不同的图像。在域适应性(DA)中,人们希望使用源标记的图像对未标记的目标图像进行分类。不幸的是,在源训练集中训练的深度神经网络在不属于训练领域的目标图像上表现不佳。改善这些性能的一种策略是使用最佳传输(OT)在嵌入式空间中对齐源和目标图像分布。但是,OT会导致负转移,即与不同标签的样品对齐,这导致过度拟合,尤其是在域之间存在标签移动的情况下。在这项工作中,我们通过将其解释为针对目标图像的嘈杂标签分配来减轻负相位。然后,我们通过适当的正则化来减轻其效果。我们建议将混合正则化\ citep {zhang2018mixup}与噪音标签强大的损失,以提高域的适应性性能。我们在一项广泛的消融研究中表明,这两种技术的结合对于提高性能至关重要。最后,我们在几个基准和现实世界DA问题上评估了称为\ textsc {mixunbot}的方法。
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在本文中,我们提出了一种使用域鉴别特征模块的双模块网络架构,以鼓励域不变的特征模块学习更多域不变的功能。该建议的架构可以应用于任何利用域不变功能的任何模型,用于无监督域适应,以提高其提取域不变特征的能力。我们在作为代表性算法的神经网络(DANN)模型的区域 - 对抗训练进行实验。在培训过程中,我们为两个模块提供相同的输入,然后分别提取它们的特征分布和预测结果。我们提出了差异损失,以找到预测结果的差异和两个模块之间的特征分布。通过对抗训练来最大化其特征分布和最小化其预测结果的差异,鼓励两个模块分别学习更多域歧视和域不变特征。进行了广泛的比较评估,拟议的方法在大多数无监督的域适应任务中表现出最先进的。
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