我们研究了高度实用但相对研究的潜在域适应性问题,其中应将源模型适应包含未标记域的混合域和域 - IRRERRELERRELERRELERVANS的目标数据集。此外,受数据隐私要求以及对适应本地数据分布的嵌入式和资源约束设备的需求的激励,我们专注于设置无馈源源域的适应到源数据集,也可以返回传播。我们的解决方案是元学习网络,能够嵌入混合相关目标数据集,并使用交叉注意力动态适应目标示例。最终的框架可导致强大的ERM基线的一致改进。我们还表明,我们的框架有时甚至在域监督适应的上限上有所改善,在这种适应中,仅提供与域相关的实例进行适应。这表明人类注释的域标签可能并不总是最佳的,并提高了通过自动实例选择做得更好的可能性。
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大多数机器学习算法的基本假设是培训和测试数据是从相同的底层分布中汲取的。然而,在几乎所有实际应用中违反了这种假设:由于不断变化的时间相关,非典型最终用户或其他因素,机器学习系统经常测试。在这项工作中,我们考虑域泛化的问题设置,其中训练数据被构造成域,并且可能有多个测试时间偏移,对应于新域或域分布。大多数事先方法旨在学习在所有域上执行良好的单一强大模型或不变的功能空间。相比之下,我们的目标是使用未标记的测试点学习适应域转移到域移的模型。我们的主要贡献是介绍自适应风险最小化(ARM)的框架,其中模型被直接优化,以便通过学习来转移以适应培训域来改编。与稳健性,不变性和适应性的先前方法相比,ARM方法提供了在表现域移位的多个图像分类问题上的性能增益为1-4%的测试精度。
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当源(训练)数据和目标(测试)数据之间存在域移动时,深网很容易降级。最近的测试时间适应方法更新了通过流数据部署在新目标环境中的预训练源模型的批归归式层,以减轻这种性能降低。尽管此类方法可以在不首先收集大型目标域数据集的情况下进行调整,但它们的性能取决于流媒体条件,例如迷你批量的大小和类别分布,在实践中可能无法预测。在这项工作中,我们提出了一个框架,以适应几个域的适应性,以应对数据有效适应的实际挑战。具体而言,我们提出了在预训练的源模型中对特征归一化统计量的约束优化,该模型由目标域的小支持集监督。我们的方法易于实现,并改善每类用于分类任务的示例较小的源模型性能。对5个跨域分类和4个语义分割数据集进行了广泛的实验表明,我们的方法比测试时间适应更准确,更可靠,同时不受流媒体条件的约束。
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当部署和培训之间存在分配变化时,深层神经网络的性能恶化严重。域的概括(DG)旨在通过仅依靠一组源域来安全地传输模型以看不见目标域。尽管已经提出了各种DG方法,但最近的一项名为Domainbed的研究表明,其中大多数没有超过简单的经验风险最小化(ERM)。为此,我们提出了一个通用框架,该框架与现有的DG算法是正交的,并且可以始终如一地提高其性能。与以前的DG作品不同的是,在静态源模型上有希望成为通用的DG,我们提出的ADAODM会在测试时间适应不同目标域的源模型。具体而言,我们在共享域形式的特征提取器上创建多个域特异性分类器。特征提取器和分类器以对抗性方式进行了训练,其中特征提取器将输入样品嵌入到域不变的空间中,并且多个分类器捕获了每个分类器与特定源域有关的独特决策边界。在测试过程中,可以通过利用源分类器之间的预测分歧来有效地衡量目标和源域之间的分布差异。通过微调源模型以最大程度地减少测试时间的分歧,目标域特征与不变特征空间很好地对齐。我们验证了两种流行的DG方法,即ERM和Coral,以及四个DG基准,即VLCS,PACS,OfficeHome和TerrainCognita。结果表明,ADAODM稳定地提高了对看不见的域的概括能力,并实现了最先进的性能。
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语义分割在广泛的计算机视觉应用中起着基本作用,提供了全球对图像​​的理解的关键信息。然而,最先进的模型依赖于大量的注释样本,其比在诸如图像分类的任务中获得更昂贵的昂贵的样本。由于未标记的数据替代地获得更便宜,因此无监督的域适应达到了语义分割社区的广泛成功并不令人惊讶。本调查致力于总结这一令人难以置信的快速增长的领域的五年,这包含了语义细分本身的重要性,以及将分段模型适应新环境的关键需求。我们提出了最重要的语义分割方法;我们对语义分割的域适应技术提供了全面的调查;我们揭示了多域学习,域泛化,测试时间适应或无源域适应等较新的趋势;我们通过描述在语义细分研究中最广泛使用的数据集和基准测试来结束本调查。我们希望本调查将在学术界和工业中提供具有全面参考指导的研究人员,并有助于他们培养现场的新研究方向。
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Domain generalization (DG) is the challenging and topical problem of learning models that generalize to novel testing domains with different statistics than a set of known training domains. The simple approach of aggregating data from all source domains and training a single deep neural network end-to-end on all the data provides a surprisingly strong baseline that surpasses many prior published methods. In this paper we build on this strong baseline by designing an episodic training procedure that trains a single deep network in a way that exposes it to the domain shift that characterises a novel domain at runtime. Specifically, we decompose a deep network into feature extractor and classifier components, and then train each component by simulating it interacting with a partner who is badly tuned for the current domain. This makes both components more robust, ultimately leading to our networks producing state-of-the-art performance on three DG benchmarks. Furthermore, we consider the pervasive workflow of using an ImageNet trained CNN as a fixed feature extractor for downstream recognition tasks. Using the Visual Decathlon benchmark, we demonstrate that our episodic-DG training improves the performance of such a general purpose feature extractor by explicitly training a feature for robustness to novel problems. This shows that DG training can benefit standard practice in computer vision.
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部署的ML模型的基本要求是从与培训不同的测试分布中汲取的数据概括。解决此问题的一个流行解决方案是,仅使用未标记的数据将预训练的模型调整为新的域。在本文中,我们关注该问题的挑战性变体,其中访问原始源数据受到限制。虽然完全测试时间适应(FTTA)和无监督的域适应性(UDA)密切相关,但由于大多数UDA方法需要访问源数据,因此UDA的进展不容易适用于TTA。因此,我们提出了一种新方法,即Cattan,它通过放松了通过新颖的深层子空间对准策略来放松访问整个源数据的需求,从而弥合了UDA和FTTA。通过为源数据存储的子空间基础设置的最小开销,Cattan在适应过程中可以在源数据和目标数据之间进行无监督的对齐。通过对多个2D和3D Vision基准测试(Imagenet-C,Office-31,OfficeHome,Domainnet,PointDa-10)和模型体系结构进行广泛的实验评估,我们在FTTA性能方面表现出显着提高。此外,即使使用固有健壮的模型,预训练的VIT表示以及目标域中的样本可用性低,我们也会对对齐目标的实用性做出许多关键发现。
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尽管进行了多年的研究,但跨域的概括仍然是深层网络的语义分割的关键弱点。先前的研究取决于静态模型的假设,即训练过程完成后,模型参数在测试时间保持固定。在这项工作中,我们通过一种自适应方法来挑战这一前提,用于语义分割,将推理过程调整为每个输入样本。自我适应在两个级别上运行。首先,它采用了自我监督的损失,该损失将网络中卷积层的参数定制为输入图像。其次,在批准层中,自适应近似于整个测试数据的平均值和方差,这是不可用的。它通过在训练和从单个测试样本得出的参考分布之间进行插值来实现这一目标。为了凭经验分析我们的自适应推理策略,我们制定并遵循严格的评估协议,以解决先前工作的严重局限性。我们的广泛分析得出了一个令人惊讶的结论:使用标准训练程序,自我适应大大优于强大的基准,并在多域基准测试方面设定了新的最先进的准确性。我们的研究表明,自适应推断可以补充培训时间的既定模型正规化实践,以改善深度网络的概括到异域数据。
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对分布(OOD)数据的概括是人类自然的能力,但对于机器而言挑战。这是因为大多数学习算法强烈依赖于i.i.d.〜对源/目标数据的假设,这在域转移导致的实践中通常会违反。域的概括(DG)旨在通过仅使用源数据进行模型学习来实现OOD的概括。在过去的十年中,DG的研究取得了长足的进步,导致了广泛的方法论,例如,基于域的一致性,元学习,数据增强或合奏学习的方法,仅举几例;还在各个应用领域进行了研究,包括计算机视觉,语音识别,自然语言处理,医学成像和强化学习。在本文中,首次提供了DG中的全面文献综述,以总结过去十年来的发展。具体而言,我们首先通过正式定义DG并将其与其他相关领域(如域适应和转移学习)联系起来来涵盖背景。然后,我们对现有方法和理论进行了彻底的审查。最后,我们通过有关未来研究方向的见解和讨论来总结这项调查。
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We demonstrate that self-learning techniques like entropy minimization and pseudo-labeling are simple and effective at improving performance of a deployed computer vision model under systematic domain shifts. We conduct a wide range of large-scale experiments and show consistent improvements irrespective of the model architecture, the pre-training technique or the type of distribution shift. At the same time, self-learning is simple to use in practice because it does not require knowledge or access to the original training data or scheme, is robust to hyperparameter choices, is straight-forward to implement and requires only a few adaptation epochs. This makes self-learning techniques highly attractive for any practitioner who applies machine learning algorithms in the real world. We present state-of-the-art adaptation results on CIFAR10-C (8.5% error), ImageNet-C (22.0% mCE), ImageNet-R (17.4% error) and ImageNet-A (14.8% error), theoretically study the dynamics of self-supervised adaptation methods and propose a new classification dataset (ImageNet-D) which is challenging even with adaptation.
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Test-time adaptation (TTA) has attracted significant attention due to its practical properties which enable the adaptation of a pre-trained model to a new domain with only target dataset during the inference stage. Prior works on TTA assume that the target dataset comes from the same distribution and thus constitutes a single homogeneous domain. In practice, however, the target domain can contain multiple homogeneous domains which are sufficiently distinctive from each other and those multiple domains might occur cyclically. Our preliminary investigation shows that domain-specific TTA outperforms vanilla TTA treating compound domain (CD) as a single one. However, domain labels are not available for CD, which makes domain-specific TTA not practicable. To this end, we propose an online clustering algorithm for finding pseudo-domain labels to obtain similar benefits as domain-specific configuration and accumulating knowledge of cyclic domains effectively. Moreover, we observe that there is a significant discrepancy in terms of prediction quality among samples, especially in the CD context. This further motivates us to boost its performance with gradient denoising by considering the image-wise similarity with the source distribution. Overall, the key contribution of our work lies in proposing a highly significant new task compound domain test-time adaptation (CD-TTA) on semantic segmentation as well as providing a strong baseline to facilitate future works to benchmark.
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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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很少有视觉识别是指从一些标记实例中识别新颖的视觉概念。通过将查询表示形式与类表征进行比较以预测查询实例的类别,许多少数射击的视觉识别方法采用了基于公制的元学习范式。但是,当前基于度量的方法通常平等地对待所有实例,因此通常会获得有偏见的类表示,考虑到并非所有实例在总结了类级表示的实例级表示时都同样重要。例如,某些实例可能包含无代表性的信息,例如过多的背景和无关概念的信息,这使结果偏差。为了解决上述问题,我们提出了一个新型的基于公制的元学习框架,称为实例自适应类别表示网络(ICRL-net),以进行几次视觉识别。具体而言,我们开发了一个自适应实例重新平衡网络,具有在生成班级表示,通过学习和分配自适应权重的不同实例中的自适应权重时,根据其在相应类的支持集中的相对意义来解决偏见的表示问题。此外,我们设计了改进的双线性实例表示,并结合了两个新型的结构损失,即,阶层内实例聚类损失和阶层间表示区分损失,以进一步调节实例重估过程并完善类表示。我们对四个通常采用的几个基准测试:Miniimagenet,Tieredimagenet,Cifar-FS和FC100数据集进行了广泛的实验。与最先进的方法相比,实验结果证明了我们的ICRL-NET的优势。
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Few-shot classification aims to recognize unlabeled samples from unseen classes given only few labeled samples. The unseen classes and low-data problem make few-shot classification very challenging. Many existing approaches extracted features from labeled and unlabeled samples independently, as a result, the features are not discriminative enough. In this work, we propose a novel Cross Attention Network to address the challenging problems in few-shot classification. Firstly, Cross Attention Module is introduced to deal with the problem of unseen classes. The module generates cross attention maps for each pair of class feature and query sample feature so as to highlight the target object regions, making the extracted feature more discriminative. Secondly, a transductive inference algorithm is proposed to alleviate the low-data problem, which iteratively utilizes the unlabeled query set to augment the support set, thereby making the class features more representative. Extensive experiments on two benchmarks show our method is a simple, effective and computationally efficient framework and outperforms the state-of-the-arts.
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域泛化(DG)利用多个标记的源数据集来训练未经化的目标域的概括模型。然而,由于昂贵的注释成本,在现实世界应用中难以满足标记所有源数据的要求。在本文中,我们调查单个标记的域泛化(SLDG)任务,只标有一个源域,这比传统的域泛化(CDG)更实用和具有挑战性。 SLDG任务中的主要障碍是可怜的概括偏置:标记源数据集中的鉴别信息可以包含特定于域的偏差,限制训练模型的泛化。为了解决这个具有挑战性的任务,我们提出了一种称为域特定偏置滤波(DSBF)的新方法,该方法用标记的源数据初始化识别模型,然后通过用于泛化改进的未标记的源数据来滤除其域特定的偏差。我们将过滤过程划分为(1)特征提取器扩展通过K-Means的基于聚类的语义特征重新提取和(2)分类器通过注意引导语义特征投影校准。 DSBF统一探索标签和未标记的源数据,以增强培训模型的可辨性和泛化,从而产生高度普遍的模型。我们进一步提供了理论分析,以验证所提出的域特定的偏置滤波过程。关于多个数据集的广泛实验显示了DSBF在解决具有挑战性的SLDG任务和CDG任务时的优越性。
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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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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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本文提出了一种新颖的测试时间适应策略,该策略仅使用来自目标域的未标记的在线数据来调整在源域上预先训练的模型,以减轻由于源和目标域之间的分布变化而导致的性能降低。使用未标记的在线数据调整整个模型参数可能是有害的,这是由于无监督目标的错误信号。为了减轻此问题,我们提出了一个偏僻的权重正则化,该调整重量正规化鼓励在很大程度上更新模型参数对分布移位敏感的参数,同时在测试时间适应期间稍微更新那些对变化的不敏感的参数。这种正则化使该模型能够通过利用高学习率的好处来快速适应目标域而无需性能降低。此外,我们提出了一个基于最近的源原型来对齐源和目标特征的辅助任务,这有​​助于减少分布转移并导致进一步的性能提高。我们表明,我们的方法在各种标准基准方面展示了最先进的性能,甚至超过其监督的对手。
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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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机器学习系统通常假设训练和测试分布是相同的。为此,关键要求是开发可以概括到未经看不见的分布的模型。领域泛化(DG),即分销概括,近年来引起了越来越令人利益。域概括处理了一个具有挑战性的设置,其中给出了一个或几个不同但相关域,并且目标是学习可以概括到看不见的测试域的模型。多年来,域概括地区已经取得了巨大进展。本文提出了对该地区最近进步的首次审查。首先,我们提供了域泛化的正式定义,并讨论了几个相关领域。然后,我们彻底审查了与域泛化相关的理论,并仔细分析了泛化背后的理论。我们将最近的算法分为三个类:数据操作,表示学习和学习策略,并为每个类别详细介绍几种流行的算法。第三,我们介绍常用的数据集,应用程序和我们的开放源代码库进行公平评估。最后,我们总结了现有文学,并为未来提供了一些潜在的研究主题。
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