冷冻电子断层扫描（Cryo-et）是一种3D成像技术，可以在近原子分辨率下原位地置于亚细胞结构。细胞冷冻剂图像有助于解决大分子的结构并在单个细胞中确定它们的空间关系，这对细胞和结构生物学具有广泛的意义。体摩数分类和识别构成了这些大分子结构的系统恢复的主要步骤。已被证明监督深度学习方法对重组分类进行高度准确和高效，而是由于缺乏注释数据而受到有限的适用性。虽然生成用于训练监督模型的模拟数据是潜在的解决方案，但与真实实验数据相比，生成数据中的图像强度分布的相当差异将导致训练有素的模型在预测真实错误谱图上预测类别中的差。在这项工作中，我们呈现了低温，一个完全无监督的域适应和随机化框架，用于深入学习的跨域重组分类。我们使用无监督的多逆境域适应来减少模拟和实验数据的特征之间的域移位。我们使用“翘曲”模块开发网络驱动的域随机化过程，以改变模拟数据，并帮助分类器在实验数据上更好地推广。我们不使用任何标记的实验数据来训练我们的模型，而一些现有的替代方法需要标记为跨域分类的实验样本。然而，在本文在本文中，使用两种模拟和实验数据在本文中显示的广泛评估研究中的横域重组分类中现有的替代方法的优先效果优异。

脑电图（EEG）解码旨在识别基于非侵入性测量的脑活动的神经处理的感知，语义和认知含量。当应用于在静态，受控的实验室环境中获取的数据时，传统的EEG解码方法取得了适度的成功。然而，开放世界的环境是一个更现实的环境，在影响EEG录音的情况下，可以意外地出现，显着削弱了现有方法的鲁棒性。近年来，由于其在特征提取的卓越容量，深入学习（DL）被出现为潜在的解决方案。它克服了使用浅架构提取的“手工制作”功能或功能的限制，但通常需要大量的昂贵，专业标记的数据 - 并不总是可获得的。结合具有域特定知识的DL可能允许开发即使具有小样本数据，也可以开发用于解码大脑活动的鲁棒方法。虽然已经提出了各种DL方法来解决EEG解码中的一些挑战，但目前缺乏系统的教程概述，特别是对于开放世界应用程序。因此，本文为开放世界EEG解码提供了对DL方法的全面调查，并确定了有前途的研究方向，以激发现实世界应用中的脑电图解码的未来研究。

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。

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。

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。

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.

睡眠分期在诊断和治疗睡眠障碍中非常重要。最近，已经提出了许多数据驱动的深度学习模型，用于自动睡眠分期。他们主要在一个大型公共标签的睡眠数据集上训练该模型，并在较小的主题上对其进行测试。但是，他们通常认为火车和测试数据是从相同的分布中绘制的，这可能在现实世界中不存在。最近已经开发了无监督的域适应性（UDA）来处理此域移位问题。但是，以前用于睡眠分期的UDA方法具有两个主要局限性。首先，他们依靠一个完全共享的模型来对齐，该模型可能会在功能提取过程中丢失特定于域的信息。其次，它们仅在全球范围内将源和目标分布对齐，而无需考虑目标域中的类信息，从而阻碍了测试时模型的分类性能。在这项工作中，我们提出了一个名为Adast的新型对抗性学习框架，以解决未标记的目标域中的域转移问题。首先，我们开发了一个未共享的注意机制，以保留两个领域中的域特异性特征。其次，我们设计了一种迭代自我训练策略，以通过目标域伪标签提高目标域上的分类性能。我们还建议双重分类器，以提高伪标签的鲁棒性和质量。在六个跨域场景上的实验结果验证了我们提出的框架的功效及其优于最先进的UDA方法。源代码可在https://github.com/emadeldeen24/adast上获得。

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.

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.

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.

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.

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

近年来，深度学习已成为遥感科学家最有效的计算机视觉工具之一。但是，遥感数据集缺乏培训标签，这意味着科学家需要解决域适应性问题，以缩小卫星图像数据集之间的差异。结果，随后训练的图像分割模型可以更好地概括并使用现有的一组标签，而不需要新的标签。这项工作提出了一个无监督的域适应模型，该模型可在样式转移阶段保留图像的语义一致性和每个像素质量。本文的主要贡献是提出了SEMI2I模型的改进体系结构，该模型显着提高了所提出的模型的性能，并使其与最先进的Cycada模型具有竞争力。第二个贡献是在遥感多波段数据集（例如Worldview-2和Spot-6）上测试Cycada模型。提出的模型可在样式传递阶段保留图像的语义一致性和每个像素质量。因此，与SEMI2I模型相比，经过适应图像的训练的语义分割模型显示出可观的性能增长，并达到与最先进的Cycada模型相似的结果。所提出方法的未来开发可能包括生态领域转移，{\ em先验}对数据分布的质量评估，或探索域自适应模型的内部体系结构。

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.

无监督的域适应性（UDA）引起了相当大的关注，这将知识从富含标签的源域转移到相关但未标记的目标域。减少域间差异一直是提高UDA性能的关键因素，尤其是对于源域和目标域之间存在较大差距的任务。为此，我们提出了一种新颖的风格感知功能融合方法（SAFF），以弥合大域间隙和转移知识，同时减轻阶级歧视性信息的丧失。受到人类传递推理和学习能力的启发，研究了一种新颖的风格感知的自我互化领域（SSID），通过一系列中级辅助综合概念将两个看似无关的概念联系起来。具体而言，我们提出了一种新颖的SSID学习策略，该策略从源和目标域中选择样本作为锚点，然后随机融合这些锚的对象和样式特征，以生成具有标记和样式丰富的中级辅助功能以进行知识转移。此外，我们设计了一个外部存储库来存储和更新指定的标记功能，以获得稳定的类功能和班级样式功能。基于提议的内存库，内部和域间损耗功能旨在提高类识别能力和特征兼容性。同时，我们通过无限抽样模拟SSID的丰富潜在特征空间，并通过数学理论模拟损失函数的收敛性。最后，我们对常用的域自适应基准测试进行了全面的实验，以评估所提出的SAFF，并且实验结果表明，所提出的SAFF可以轻松地与不同的骨干网络结合在一起，并获得更好的性能作为插入插型模块。

由于多路径干扰（MPI），飞行时间（TOF）摄像机受高水平的噪声和扭曲。虽然最近的研究表明，2D神经网络能够以先前的传统最先进的（SOTA）方法胜过去噪，但已经完成了基于学习的方法的研究，以便直接使用存在的3D信息在深度图像中。在本文中，我们提出了一种在3D空间中运行的迭代去噪方法，该方法旨在通过启用3D点卷积来校正视图方向校正点的位置来学习2.5D数据。由于标记的现实世界数据稀缺了这项任务，我们进一步培训我们的网络，并在未标记的真实世界数据上培训我们的网络，以解释现实世界统计数据。我们展示我们的方法能够在多个数据集中倾斜SOTA方法，包括两个现实世界数据集和本文介绍的新的大规模合成数据集。

我们在本文中解决了增量无监督域适应（IDA）的新问题。我们假设标记的源域和不同的未标记的目标域通过约束逐步观察到与当前域的数据仅一次可用。目标是为当前域概括为所有过去域的准确性。 IDA设置因域之间的突然差异以及包括源域内的过去数据的不可用。受到生成功能重放的概念的启发，我们提出了一种名为特征重放的增量域适应（Frida）的新颖框架，它利用了一个名为域 - 通用辅助分类GaN（DGAC-GaN）的新的增量生成对抗性网络（GAN）来生产域明确的特征表示无缝。对于域对齐，我们提出了一种简单的扩展名为Dann-Ib的流行域对抗神经网络（Dann），鼓励歧视域 - 不变和任务相关的特征学习。 Office-Home，Office-Caltech和Domainnet数据集的实验结果证实，FIDA维护了卓越的稳定性可塑性权衡，而不是文献。

最近的智能故障诊断（IFD）的进展大大依赖于深度代表学习和大量标记数据。然而，机器通常以各种工作条件操作，或者目标任务具有不同的分布，其中包含用于训练的收集数据（域移位问题）。此外，目标域中的新收集的测试数据通常是未标记的，导致基于无监督的深度转移学习（基于UDTL为基础的）IFD问题。虽然它已经实现了巨大的发展，但标准和开放的源代码框架以及基于UDTL的IFD的比较研究尚未建立。在本文中，我们根据不同的任务，构建新的分类系统并对基于UDTL的IFD进行全面审查。对一些典型方法和数据集的比较分析显示了基于UDTL的IFD中的一些开放和基本问题，这很少研究，包括特征，骨干，负转移，物理前导等的可转移性，强调UDTL的重要性和再现性 - 基于IFD，整个测试框架将发布给研究界以促进未来的研究。总之，发布的框架和比较研究可以作为扩展界面和基本结果，以便对基于UDTL的IFD进行新的研究。代码框架可用于\ url {https:/github.com/zhaozhibin/udtl}。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

对大脑的电子显微镜（EM）体积的精确分割对于表征细胞或细胞器水平的神经元结构至关重要。尽管有监督的深度学习方法在过去几年中导致了该方向的重大突破，但它们通常需要大量的带注释的数据才能接受培训，并且在类似的实验和成像条件下获得的其他数据上的表现不佳。这是一个称为域适应的问题，因为从样本分布（或源域）中学到的模型难以维持其对从不同分布或目标域提取的样品的性能。在这项工作中，我们解决了基于深度学习的域适应性的复杂案例，以跨不同组织和物种的EM数据集进行线粒体分割。我们提出了三种无监督的域适应策略，以根据（1）两个域之间的最新样式转移来改善目标域中的线粒体分割； （2）使用未标记的源和目标图像预先培训模型的自我监督学习，然后仅用源标签进行微调； （3）具有标记和未标记图像的端到端训练的多任务神经网络体系结构。此外，我们提出了基于在源域中仅获得的形态学先验的新训练停止标准。我们使用三个公开可用的EM数据集进行了所有可能的跨数据库实验。我们评估了目标数据集预测的线粒体语义标签的拟议策略。此处介绍的方法优于基线方法，并与最新的状态相比。在没有验证标签的情况下，监视我们提出的基于形态的度量是停止训练过程并在平均最佳模型中选择的直观有效的方法。