传统的无监督域自适应(UDA)假设训练数据是从单个域采样的。这忽略了从多个来源收集的更实际的场景训练数据,需要多源域适应。我们为解决这一问题做出了三大贡献。首先,我们提出了一种新的深度学习方法,即多源域自适应的时间匹配M3SDA,旨在通过动态调整其特征分布的时刻,将从多个标记源域学到的知识转移到未标记的目标域。其次,我们为多源域适应的矩相关误差界提供了合理的理论分析。第三,我们收集并注释了迄今为止最大的UDAdataset六个不同的域和大约60万个图像分布在345个类别中,解决了多源UDA研究中数据可用性的差距。进行了广泛的实验,以证明我们提出的模型的有效性,该模型大大优于现有的最先进的方法。
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用于图像分类任务的深度学习(例如,卷积神经网络)的准确性严格依赖于标记的训练数据的量。旨在解决缺少标记数据但获得对廉价可用未标记数据的访问的新域上的图像分类任务,通过假设来自不同域的图像具有不变特征,无监督域适应是提高性能而不产生额外标记成本的有前景的技术。在本文中,我们提出了一种新的无监督域自适应方法,称为深度神经网络的域 - 对抗残差传递(DART)学习,以解决跨域图像分类问题。与现有的无监督域自适应方法相比,所提出的DART不仅通过对抗训练来学习域不变特征,而且还通过遗传 - 转移策略实现了强大的域自适应分类,所有这些都在端到端的训练框架中。我们在几个众所周知的基准数据集上评估了所提出的跨域图像分类任务方法的性能,其中我们的方法明显优于最先进的方法。
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Current Domain Adaptation (DA) methods based on deep architectures assume that the source samples arise from a single distribution. However, in practice most datasets can be regarded as mixtures of multiple domains. In these cases exploiting single-source DA methods for learning target classifiers may lead to sub-optimal, if not poor, results. In addition, in many applications it is difficult to manually provide the domain labels for all source data points, i.e. latent domains should be automatically discovered. This paper introduces a novel Convolutional Neural Network (CNN) architecture which (i) automatically discovers latent domains in visual datasets and (ii) exploits this information to learn robust target classifiers. Our approach is based on the introduction of two main components, which can be embedded into any existing CNN architecture: (i) a side branch that automatically computes the assignment of a source sample to a latent domain and (ii) novel layers that exploit domain membership information to appropriately align the distribution of the CNN internal feature representations to a reference distribution. We test our approach on publicly-available datasets, showing that it outperforms state-of-the-art multi-source DA methods by a large margin.
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无监督域适应的目的是在给定标记的源数据集和未标记的目标数据集的情况下学习目标域的强大分类器。为了减轻域移位的主要挑战“域移位”的影响,研究试图调整分布的结果。 twodomains。最近的研究表明,生成对抗网络(GAN)具有隐式捕获数据分布的能力。因此,在本文中,我们提出了一个简单但有效的无监督域适应模型,利用对抗性学习。在源域和目标域之间共享相同的编码器,期望在对抗性鉴别器的帮助下提取域不变表示。利用标记的源数据,我们引入中心损失以增加所学特征的判别力。我们进一步调整了两个域的条件分布,以强制区分目标域中的特征。与先前使用固定的预训练编码器提取源特征的研究不同,我们的方法联合学习两个域的特征表示。此外,通过共享编码器,模型不需要在测试期间知道图像的来源,因此可以更广泛地应用。我们在几个无监督的域自适应基准上评估所提出的方法,并获得与最先进结果相当的优越或相当的性能。
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现有的域自适应方法通常假设不同的域具有相同的标签空间,这对于实际应用是非常有限的。在本文中,我们关注开放的域域适应的更现实和具有挑战性的情况。特别是,在开放集域适应中,我们允许来自源域和目标域的类部分重叠。在这种情况下,由于两个域中的标签空间不同,传统分布对齐的假设不再适用。为了应对这一挑战,我们提出了一种新方法,它被称为已知类自觉集合(KASE),它建立在最近开发的自集合模型之上。在InKASE中,我们首先引入一个知名类意识识别(KAR)模块来识别目标域中的已知和未知类,这是通过鼓励已知类的低交叉熵和基于来自未知类的源数据的高熵来实现的。 。然后,我们开发了一个知识级的意识适应(KAA)模块,通过重新权衡基于KAR预测的已知类别的未标记目标样本的可能性,从源域到目标更好地适应。在多个基准数据集上进行了大量实验证明我们的方法的有效性。
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我们提出了一种新颖,统一的深度学习框架,能够从跨多个域的数据中学习领域不变表示。通过对抗性训练和利用特定领域信息的额外能力进行学习,所提出的网络能够执行连续的跨域图像转换和操作,并相应地产生理想的outputimages。此外,所得到的特征表示表现出无监督域自适应的优越性能,这也验证了所提出的模型在学习用于描述跨域数据的解缠结特征方面的有效性。
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The objective of unsupervised domain adaptation is to leverage features from a labeled source domain and learn a classifier for an unlabeled target domain, with a similar but different data distribution. Most deep learning approaches to domain adaptation consist of two steps: (i) learn features that preserve a low risk on labeled samples (source domain) and (ii) make the features from both domains to be as indistinguishable as possible, so that a clas-sifier trained on the source can also be applied on the target domain. In general, the classifiers in step (i) consist of fully-connected layers applied directly on the indistinguishable features learned in (ii). In this paper, we propose a different way to do the classification, using similarity learning. The proposed method learns a pairwise similarity function in which classification can be performed by computing similarity between prototype representations of each category. The domain-invariant features and the categorical prototype representations are learned jointly and in an end-to-end fashion. At inference time, images from the target domain are compared to the prototypes and the label associated with the one that best matches the image is out-puted. The approach is simple, scalable and effective. We show that our model achieves state-of-the-art performance in different unsupervised domain adaptation scenarios.
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域自适应方法的主要目标是将知识从源域转移到具有相似但不同的数据分布的目标域。因此,为了正确地对未标记的目标域样本进行分类,标准方法是学习源域和目标域的共同表示,从而间接解决在目标域中学习分类器的问题。然而,这种方法不直接在目标域中添加分类任务。相反,我们提出了一种直接解决未标记目标域中学习分类器问题的方法。特别是,我们训练一个分类正确地对训练样本进行分类,同时以无监督的方式对目标域中的样本进行分类。相应的模型被称为域适应的判别编码(DEDA)。我们表明这种执行无监督域自适应的简单方法非常强大。我们的方法在各种图像分类基准上的无监督调整任务中实现了最先进的结果。 Wealso在Amazonreviews情绪分类数据集中获得了域适应的最新表现。当源数据具有较少标记的示例以及零目标域适应任务时,我们执行额外的实验,其中没有目标域样本用于训练。
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It is important to transfer the knowledge from label-rich source domain to unlabeled target domain due to the expensive cost of manual labeling efforts. Prior domain adaptation methods address this problem through aligning the global distribution statistics between source domain and target domain, but a drawback of prior methods is that they ignore the semantic information contained in samples, e.g., features of backpack-s in target domain might be mapped near features of cars in source domain. In this paper, we present moving semantic transfer network, which learn semantic representations for unlabeled target samples by aligning labeled source centroid and pseudo-labeled target centroid. Features in same class but different domains are expected to be mapped nearby, resulting in an improved target classification accuracy. Moving average cen-troid alignment is cautiously designed to compensate the insufficient categorical information within each mini batch. Experiments testify that our model yields state of the art results on standard datasets.
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深度域自适应的最新进展表明,对抗性学习可以嵌入到深度网络中,以学习可传递的特征,这些特征可以减少源域和目标域之间的分布差异。现有的基于单域鉴别器的域对等自适应方法仅在不利用复数多模结构的情况下对源数据分布和目标数据分布进行了分析。在本文中,我们提出了一种多对抗域适应(MADA)方法,该方法捕获多模结构,以基于多个domaindisciminiminator实现不同数据分布的细粒度对齐。可以通过随机梯度下降来实现自适应,其中梯度通过在线性时间中的反向传播来计算。经验证据表明,所提出的模型在标准域适应数据集上优于现有技术方法。
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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-to-image 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.
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最近,相当大的努力致力于深度域适应计算机视觉和机器学习社区。然而,大多数现有工作仅通过最小化不同域之间的分布差异来集中于学习共享特征表示。由于所有域对齐方法都只能减少但不能删除域移位。分布在簇的边缘附近或远离其相应的类中心的目标域样本很容易被从源域学习的超平面错误分类。为了缓解这个问题,我们提出了联合域对齐和判别特征学习,这可能有利于域对齐和最终分类。具体地,提出了基于实例的判别特征学习方法和基于中心的判别特征学习方法,两者都保证了具有更好的类内紧致性和类间可分性的域不变特征。大量实验表明,学习共享特征空间中的差异特征可以显着提高深域自适应方法的性能。
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无监督域适应旨在减少将知识从监督源域转移到无监督目标域的域转移。已经成功探索了对抗特征对齐,以最小化域差异。然而,当两个域不共享相同的标签空间时,现有方法通常难以优化混合学习目标并且易受负转移的影响。在本文中,我们凭经验揭示了目标域的不规则区分主要反映在其相对于源域的特征范数值低得多的特征范数值中。我们提出了一种非参数自适应特征范数AFN方法,它独立于两个域的标签空间之间的关联。我们证明,调整源和目标域的特征规范以在大范围的值上实现均衡可能导致不显着的域转移增益。没有花里胡哨,只有几行代码,我们的方法在很大程度上解除了对目标域的歧视(在VisDA2017中仅来自Source的23.7%)并实现了香草环境的新技术。此外,由于我们的方法不需要对特征分布进行有意识的对齐,因此对于负转换而言,它可以在部分设置下超出现有方法的极大幅度(Office-Home为9.8 \%,VisDA2017为14.1 \%)。代码可在https://github.com/jihanyang/AFN获得。我们对我们方法的可用性负责。
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Deep-layered models trained on a large number of labeled samples boost the accuracy of many tasks. It is important to apply such models to different domains because collecting many labeled samples in various domains is expensive. In un-supervised domain adaptation, one needs to train a classifier that works well on a target domain when provided with labeled source samples and unlabeled target samples. Although many methods aim to match the distributions of source and target samples, simply matching the distribution cannot ensure accuracy on the target domain. To learn discriminative representations for the target domain, we assume that artificially labeling target samples can result in a good representation. Tri-training leverages three classifiers equally to give pseudo-labels to unlabeled samples, but the method does not assume labeling samples generated from a different domain. In this paper, we propose an asymmetric tri-training method for unsupervised domain adaptation, where we assign pseudo-labels to unlabeled samples and train neural networks as if they are true labels. In our work, we use three networks asymmetrically. By asymmetric, we mean that two networks are used to label unlabeled target samples and one network is trained by the samples to obtain target-discriminative representations. We evaluate our method on digit recognition and sentiment analysis datasets. Our proposed method achieves state-of-the-art performance on the benchmark digit recognition datasets of domain adaptation.
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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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Classifiers trained on given databases perform poorly when tested on dataacquired in different settings. This is explained in domain adaptation througha shift among distributions of the source and target domains. Attempts to alignthem have traditionally resulted in works reducing the domain shift byintroducing appropriate loss terms, measuring the discrepancies between sourceand target distributions, in the objective function. Here we take a differentroute, proposing to align the learned representations by embedding in any givennetwork specific Domain Alignment Layers, designed to match the source andtarget feature distributions to a reference one. Opposite to previous workswhich define a priori in which layers adaptation should be performed, ourmethod is able to automatically learn the degree of feature alignment requiredat different levels of the deep network. Thorough experiments on differentpublic benchmarks, in the unsupervised setting, confirm the power of ourapproach.
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深度学习提出了希望和期望,作为许多应用程序的一般解决方案;事实证明它已被证明是有效的,但它也显示出对大量数据的强烈依赖性。幸运的是,已经证明,即使数据稀缺,也可以通过重复使用priorknowledge来训练成功的模型。因此,在最广泛的定义中,开发转移学习技术是部署有效和准确的智能系统的关键因素。本文将重点研究一系列适用于视觉目标识别任务的转移学习方法,特别是图像分类。转移学习是一个通用术语,并且特定设置已经给出了特定的名称:当学习者只能访问来自目标域的标记数据和来自不同域(源)的标记数据时,问题被称为“无监督域适应”。 (DA)。这项工作的第一部分将集中在这个设置的三种方法:其中一种方法涉及特征,一种是图像,而第三种方法同时使用两种。第二部分将重点关注机器人感知的现实生活问题,特别是RGB-D识别。机器人平台通常不仅限于色彩感知;他们经常带着Depthcamera。不幸的是,深度模态很少用于视觉识别,因为缺乏预先训练的模型,从中可以传输并且很少有数据从头开始。将提出两种处理这种情况的方法:一种使用合成数据,另一种利用跨模态转移学习。
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最近对生成性对抗网络(GAN)的兴趣越来越大,这些网络为生成建模,密度估计和能量函数学习提供了强大的功能。 GAN难以训练和评估,但能够创建令人惊讶的逼真的合成图像数据。源自GAN的想法,例如对抗性损失,正在为域适应等其他挑战创造研究机会。在本文中,我们着眼于GAN的领域,重点是新兴研究的这些领域。为了提供对抗技术的背景,我们调查了GAN领域,查看了原始配方,培训变体,评估方法和扩展。然后,我们调查了最近关于转移学习的工作,重点是比较不同的对抗领域适应方法。最后,我们期待确定GAN和域适应的开放研究方向,包括一些有前途的应用,如基于传感器的人体行为建模。
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Contemporary domain adaptation methods are very effective at aligning feature distributions of source and target domains without any target supervision. However, we show that these techniques perform poorly when even a few labeled examples are available in the target domain. To address this semi-supervised domain adaptation (SSDA) setting, we propose a novel Minimax Entropy (MME) approach that adversarially optimizes an adaptive few-shot model. Our base model consists of a feature encoding network , followed by a classification layer that computes the features' similarity to estimated prototypes (representatives of each class). Adaptation is achieved by alternately maximizing the conditional entropy of unlabeled target data with respect to the classifier and minimizing it with respect to the feature encoder. We empirically demonstrate the superiority of our method over many baselines, including conventional feature alignment and few-shot methods, setting a new state of the art for SSDA.
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提出了无监督域自适应的任务,以将标签丰富域(源域)的知识转移到标签稀缺域(目标域)。不同域之间的匹配特征分布是上述任务的广泛应用方法。但是,当两个域中的类不相同时,该方法不能很好地执行。具体地,当目标的类对应于源的类的子集时,目标样本可能与仅存在于源中的类不正确地对齐。这个问题设置被称为部分域自适应(PDA)。在这项研究中,我们提出了一种新的方法,称为PDA的两个加权不一致性减少网络(TWIN)。我们利用两个分类网络来估计每个类别中的目标样本的比例,其中加权分类损失被加权以适应目标域中存在的类别。此外,为了提取目标的判别特征,我们建议最小化由目标样本上的分类器不一致性测量的域之间的差异。我们凭经验证明,降低两个网络之间的不一致性对于PDA是有效的,并且我们的方法在几个数据集中的表现优于其他现有方法。
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