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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In this work, we connect two distinct concepts for unsupervised domain adaptation: feature distribution alignment between domains by utilizing the task-specific decision boundary [58] and the Wasserstein metric [73]. Our proposed sliced Wasserstein discrepancy (SWD) is designed to capture the natural notion of dissimilarity between the outputs of task-specific classifiers. It provides a geometrically meaningful guidance to detect target samples that are far from the support of the source and enables efficient distribution alignment in an end-to-end trainable fashion. In the experiments, we validate the effectiveness and genericness of our method on digit and sign recognition, image classification, semantic segmentation, and object detection.
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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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We consider the problem of unsupervised domain adaptation in semantic segmentation. A key in this campaign consists in reducing the domain shift, i.e., enforcing the data distributions of the two domains to be similar. One of the common strategies is to align the marginal distribution in the feature space through adversarial learning. However, this global alignment strategy does not consider the category-level joint distribution. A possible consequence of such global movement is that some categories which are originally well aligned between the source and target may be incorrectly mapped, thus leading to worse segmentation results in target domain. To address this problem, we introduce a category-level adversarial network, aiming to enforce local semantic consistency during the trend of global alignment. Our idea is to take a close look at the category-level joint distribution and align each class with an adaptive adversarial loss. Specifically, we reduce the weight of the adversarial loss for category-level aligned features while increasing the adversarial force for those poorly aligned. In this process, we decide how well a feature is category-level aligned between source and target by a co-training approach. In two domain adaptation tasks, i.e., GTA5 → Cityscapes and SYN-THIA → Cityscapes, we validate that the proposed method matches the state of the art in segmentation accuracy.
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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.
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Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains. They also can improve recognition despite the presence of domain shift or dataset bias: several adversarial approaches to unsupervised domain adaptation have recently been introduced, which reduce the difference between the training and test domain distributions and thus improve generalization performance. Prior generative approaches show compelling visualizations, but are not optimal on discriminative tasks and can be limited to smaller shifts. Prior discriminative approaches could handle larger domain shifts, but imposed tied weights on the model and did not exploit a GAN-based loss. We first outline a novel generalized framework for adversarial adaptation, which subsumes recent state-of-the-art approaches as special cases, and we use this generalized view to better relate the prior approaches. We propose a previously unexplored instance of our general framework which combines discriminative modeling, untied weight sharing, and a GAN loss, which we call Adversarial Discriminative Domain Adaptation (ADDA). We show that ADDA is more effective yet considerably simpler than competing domain-adversarial methods, and demonstrate the promise of our approach by exceeding state-of-the-art unsupervised adaptation results on standard cross-domain digit classification tasks and a new more difficult cross-modality object classification task.
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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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We propose an approach for unsupervised adaptation of object detectors from label-rich to label-poor domains which can significantly reduce annotation costs associated with detection. Recently, approaches that align distributions of source and target images using an adversarial loss have been proven effective for adapting object classifiers. However, for object detection, fully matching the entire distributions of source and target images to each other at the global image level may fail, as domains could have distinct scene layouts and different combinations of objects. On the other hand, strong matching of local features such as texture and color makes sense, as it does not change category level semantics. This motivates us to propose a novel method for detector adaptation based on strong local alignment and weak global alignment. Our key contribution is the weak alignment model, which focuses the adversarial alignment loss on images that are globally similar and puts less emphasis on aligning images that are globally dissimilar. Additionally, we design the strong domain alignment model to only look at local receptive fields of the feature map. We empirically verify the effectiveness of our method on four datasets comprising both large and small domain shifts. Our code is available at https://github.com/ VisionLearningGroup/DA_Detection.
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Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts. Recent work has shown that generative adversarial networks combined with cycle-consistency constraints are surprisingly effective at mapping images between domains, even without the use of aligned image pairs. We propose a novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model. CyCADA adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs. Our model can be applied in a variety of visual recognition and prediction settings. We show new state-of-the-art results across multiple adaptation tasks, including digit classification and semantic segmentation of road scenes demonstrating transfer from synthetic to real world domains.
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从理论上讲,无监督的域适应性(UDA)的成功在很大程度上取决于域间隙估计。但是,对于无源UDA,在适应过程中无法访问源域数据,这在测量域间隙方面构成了巨大挑战。在本文中,我们建议使用许多分类器来学习源域决策边界,即使两个域数据无法同时访问,它也提供了域间隙的更紧密的上限。对源模型进行了训练,可以推开每对分类器,同时确保决策边界的正确性。从这个意义上讲,我们的许多分类器模型尽可能将源不同类别分开,从而诱导目标域中许多分类器的最大分歧,从而最大程度地提高了可转移的源域知识。为了进行适应,源模型适应最大化分类器对之间的一致性。因此,目标特征从决策范围中推开。在UDA的几个数据集上进行的实验表明,我们的方法在免费的UDA方法中实现了最先进的性能,甚至可以竞争为可用的UDA方法竞争。
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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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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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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。
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Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks. An appealing alternative is to render synthetic data where ground-truth annotations are generated automatically. Unfortunately, models trained purely on rendered images often fail to generalize to real images. To address this shortcoming, prior work introduced unsupervised domain adaptation algorithms that attempt to map representations between the two domains or learn to extract features that are domain-invariant. In this work, we present a new approach that learns, in an unsupervised manner, a transformation in the pixel space from one domain to the other. Our generative adversarial network (GAN)-based model adapts source-domain images to appear as if drawn from the target domain. Our approach not only produces plausible samples, but also outperforms the state-of-the-art on a number of unsupervised domain adaptation scenarios by large margins. Finally, we demonstrate that the adaptation process generalizes to object classes unseen during training.
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Semantic segmentation is a key problem for many computer vision tasks. While approaches based on convolutional neural networks constantly break new records on different benchmarks, generalizing well to diverse testing environments remains a major challenge. In numerous real world applications, there is indeed a large gap between data distributions in train and test domains, which results in severe performance loss at run-time. In this work, we address the task of unsupervised domain adaptation in semantic segmentation with losses based on the entropy of the pixel-wise predictions. To this end, we propose two novel, complementary methods using (i) an entropy loss and (ii) an adversarial loss respectively. We demonstrate state-of-theart performance in semantic segmentation on two challenging "synthetic-2-real" set-ups 1 and show that the approach can also be used for detection.
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Convolutional neural network-based approaches for semantic segmentation rely on supervision with pixel-level ground truth, but may not generalize well to unseen image domains. As the labeling process is tedious and labor intensive, developing algorithms that can adapt source ground truth labels to the target domain is of great interest. In this paper, we propose an adversarial learning method for domain adaptation in the context of semantic segmentation. Considering semantic segmentations as structured outputs that contain spatial similarities between the source and target domains, we adopt adversarial learning in the output space. To further enhance the adapted model, we construct a multi-level adversarial network to effectively perform output space domain adaptation at different feature levels. Extensive experiments and ablation study are conducted under various domain adaptation settings, including synthetic-to-real and cross-city scenarios. We show that the proposed method performs favorably against the stateof-the-art methods in terms of accuracy and visual quality.
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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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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/
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深度学习极大地提高了语义细分的性能,但是,它的成功依赖于大量注释的培训数据的可用性。因此,许多努力致力于域自适应语义分割,重点是将语义知识从标记的源域转移到未标记的目标域。现有的自我训练方法通常需要多轮训练,而基于对抗训练的另一个流行框架已知对超参数敏感。在本文中,我们提出了一个易于训练的框架,该框架学习了域自适应语义分割的域不变原型。特别是,我们表明域的适应性与很少的学习共享一个共同的角色,因为两者都旨在识别一些从大量可见数据中学到的知识的看不见的数据。因此,我们提出了一个统一的框架,用于域适应和很少的学习。核心思想是使用从几个镜头注释的目标图像中提取的类原型来对源图像和目标图像的像素进行分类。我们的方法仅涉及一个阶段训练,不需要对大规模的未经通知的目标图像进行培训。此外,我们的方法可以扩展到域适应性和几乎没有射击学习的变体。关于适应GTA5到CITYSCAPES和合成景观的实验表明,我们的方法实现了对最先进的竞争性能。
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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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