Domain consistency regularization for unsupervised multi-source domain adaptive classification

Multisource unsupervised domain adaptation (MUDA) is an important and challenging topic for target classification with the assistance of labeled data in source domains. When we have several labeled source domains, it is difficult to map all source domains and target domain into a common feature space for classifying the targets well. In this article, a new progressive multisource domain adaptation network (PMSDAN) is proposed to further improve the classification performance. PMSDAN mainly consists of two steps for distribution alignment. First, the multiple source domains are integrated as one auxiliary domain to match the distribution with the target domain. By doing this, we can generally reduce the distribution discrepancy between each source and target domains, as well as the discrepancy between different source domains. It can efficiently explore useful knowledge from the integrated source domain. Second, to mine assistance knowledge from each source domain as much as possible, the distribution of the target domain is separately aligned with that of each source domain. A weighted fusion method is employed to combine the multiple classification results for making the final decision. In the optimization of domain adaption, weighted hybrid maximum mean discrepancy (WHMMD) is proposed, and it considers both the interclass and intraclass discrepancies. The effectiveness of the proposed PMSDAN is demonstrated in the experiments comparing with some state-of-the-art methods.

Deep Joint Semantic Adaptation Network for Multi-source Unsupervised Domain Adaptation

Sensors are devices that output signals for sensing physical phenomena and are widely used in all aspects of our social production activities. The continuous recording of physical parameters allows effective analysis of the operational status of the monitored system and prediction of unknown risks. Thanks to the development of deep learning, the ability to analyze temporal signals collected by sensors has been greatly improved. However, models trained in the source domain do not perform well in the target domain due to the presence of domain gaps. In recent years, many researchers have used deep unsupervised domain adaptation techniques to address the domain gap between signals collected by sensors in different scenarios, i.e., using labeled data in the source domain and unlabeled data in the target domain to improve the performance of models in the target domain. This survey first summarizes the background of recent research on unsupervised domain adaptation with time series sensor data, the types of sensors used, the domain gap between the source and target domains, and commonly used datasets. Then, the paper classifies and compares different unsupervised domain adaptation methods according to the way of adaptation and summarizes different adaptation settings based on the number of source and target domains. Finally, this survey discusses the challenges of the current research and provides an outlook on future work. This survey systematically reviews and summarizes recent research on unsupervised domain adaptation for time series sensor data to provide the reader with a systematic understanding of the field.

Bionic limb control through myoelectric pattern recognition, offering intuitive decoding of motor intent, can improve the quality of life for individuals with amputations. However, most work on pattern recognition only uses a small subset of the myoelectric data generated during daily life, to train an Artificial Neural Network (ANN) via Supervised Learning (SL). Scenarios substantially different from the recording session e.g. different limb positions, can lead to misclassifications by the ANN during everyday usage of the bionic limb. Recording labeled data from all scenarios encountered in daily life could alleviate the problem, but would be prohibitively time consuming. Unsupervised Domain Adaptation (UDA) offers a solution by leveraging unlabeled data from a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">target domain</i>, not represented in the labeled dataset i.e. the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">source domain</i>, to calibrate ANNs for improved performance. In this study we explore the potential of two UDA algorithms for domain shifts in myoelectric pattern recognition: Domain Adversarial Neural Networks (DANN) and Sliced Wasserstein Discrepancy (SWD). Offline evaluation identified SWD as the best-performing algorithm, which was subsequently validated in online experiments with 11 participants. Using UDA improved the performance on the target domain by 19% compared to an ANN trained through SL on data from the source domain only. Indeed, it nearly matched the performance of an ANN trained through SL on labeled data from both the source and target domain. This is the first time UDA was validated in online experiments as a viable approach to overcome domain shift problems caused by changes in limb position.

Remaining useful life prediction under variable operating conditions via multisource adversarial domain adaptation networks

Dual contrastive universal adaptation network for multi-source visual recognition

In unsupervised domain adaptation (UDA), a classifier for a target domain is trained with labeled source data and unlabeled target data. Existing UDA methods assume that the source data come from the same source domain (i.e., single-source scenario) or from multiple source domains, whose feature spaces have the same dimension ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">homogeneous</i> ) but different distributions (i.e., multihomogeneous-source scenario). However, in the real world, for a specific target domain, we probably have multiple different-dimension ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heterogeneous</i> ) source domains, which do not satisfy the assumption of existing UDA methods. To remove this assumption and move forward to a realistic UDA problem, this article presents a shared-fuzzy-equivalence-relation neural network (SFERNN) for addressing the multisource heterogeneous UDA problem. The SFERNN is a five-layer neural network containing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> source branches and one target branch. The network structure of the SFERNN is first confirmed by a novel fuzzy relation called multisource shared fuzzy equivalence relation. Then, we optimize parameters of the SFERNN via minimizing cross-entropy loss on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> source branches and the distributional discrepancy between each source branch and the target branch. Experiments distributed across eight real-world datasets are conducted to validate the SFERNN. This testing regime demonstrates that the SFERNN outperforms the existing single-source heterogeneous UDA methods, especially when the target domain contains few data.

Unsupervised deep domain adaptation algorithm for video based human activity recognition via recurrent neural networks

Multi-source adversarial transfer learning for ultrasound image segmentation with limited similarity

With the rapid development of the remote sensing monitoring and computer vision technology, the deep learning method has made a great progress to achieve applications such as earth observation, climate change and even space exploration. However, the model trained on existing data cannot be directly used to handle the new remote sensing data, and labeling the new data is also time-consuming and labor-intensive. Unsupervised Domain Adaptation (UDA) is one of the solutions to the aforementioned problems of labeled data defined as the source domain and unlabeled data as the target domain, i.e., its essential purpose is to obtain a well-trained model and tackle the problem of data distribution discrepancy defined as the domain shift between the source and target domain. There are a lot of reviews that have elaborated on UDA methods based on natural data, but few of these studies take into consideration thorough remote sensing applications and contributions. Thus, in this paper, in order to explore the further progress and development of UDA methods in remote sensing, based on the analysis of the causes of domain shift, a comprehensive review is provided with a fine-grained taxonomy of UDA methods applied for remote sensing data, which includes Generative training, Adversarial training, Self-training and Hybrid training methods, to better assist scholars in understanding remote sensing data and further advance the development of methods. Moreover, remote sensing applications are introduced by a thorough dataset analysis. Meanwhile, we sort out definitions and methodology introductions of partial, open-set and multi-domain UDA, which are more pertinent to real-world remote sensing applications. We can draw the conclusion that UDA methods in the field of remote sensing data are carried out later than those applied in natural images, and due to the domain gap caused by appearance differences, most of methods focus on how to use generative training (GT) methods to improve the model’s performance. Finally, we describe the potential deficiencies and further in-depth insights of UDA in the field of remote sensing.

The adversarial domain adaptation has made many achievements in the field of Unsupervised Domain Adaptation (UDA). However, initial adversarial domain adaptation methods do not consider the category-level domain adaptation, causing confusion between different categories in the different domains. This paper propose a UDA approach called Minimizing Outputs' Differences of Classifiers with Different Responsibilities (MODCDR) to improve this situation. Its framework consists of one generator and two different task-specific classifiers, one working on the source domain, while the other working on the target domain. By training the feature generator to minimize the outputs' discrepancy of these two classifiers, this method not only generates discriminative features, but also achieves the category-level feature distribution alignment simultaneously. In addition, these two classifiers can also produce and filter the reliable pseudo-labels on the target domain, being used for assisting our training. This paper tested this method on three publicly available transfer learning object recognition datasets, with comparison to recent unsupervised adversarial domain adaptation approaches. The results show that the proposed method has superior performance.

When deep neural networks are deployed in a highly automated vehicle for environment perception tasks in an unseen (target) domain that differs from the training (source) domain, the mismatch will result in decreased performance. Domain adaptation methods aim at overcoming this mismatch. Many recently investigated methods for unsupervised domain adaptation train a model using labeled source data and unlabeled target data at the same time. These methods assume that data from the target domain is available during the source domain training, which is not always the case in real applications. In this paper we present a way to perform an online style transfer for continual domain adaptation which improves performance on (multiple) unseen target domains using a given perception model. The approach is based on an image style transfer in the frequency domain and requires neither an adjustment of the given source-trained model parameters to the target domain, nor does it require any considerable amount of memory for storing its frequency domain representation of the source domain style, which is particularly important considering the hardware limitations in an autonomous vehicle.

Unsupervised Domain Adaptation (UDA) for semantic segmentation has received widespread attention for its ability to transfer knowledge from the source to target domains without a high demand for annotations. However, semantic segmentation under adverse conditions still poses significant challenges for autonomous driving, as bad weather observation data may introduce unforeseeable problems. Although previous UDA works are devoted to adverse scene tasks, their adaptation process is redundant. For instance, unlabeled snow scene training data is a must for the model to achieve fair segmentation performance in snowy scenarios. We propose calling this type of adaptation process the Single to Single (STS) strategy. Clearly, STS is time-consuming and may show weaknesses in some comprehensive scenes, such as a night scene of sleet. Motivated by the concept of Domain Generalization (DG), we propose the Single to All (STA) model. Unlike DG, which trains models on one or multiple source domains without target domains, the STA model is based on UDA and employs one source domain, one target domain, and one introduced domain to achieve generalization to all adverse conditions by training on a single-scene dataset. Specifically, the STA model is advantageous as it learns from the source domain, reserves the style factors via a Reservation domain, and adapts the unified factors by the Randomization module. An Output Space Refusion module is also further incorporated to strengthen STA. Our STA achieves state-of-the-art performance in the Foggy Driving benchmark and demonstrates great domain generalizability in all conditions of the ACDC and Foggy Zurich benchmarks.

The prime challenge in unsupervised domain adaptation (DA) is to mitigate the domain shift between the source and target domains. Prior DA works show that pretext tasks could be used to mitigate this domain shift by learning domain invariant representations. However, in practice, we find that most existing pretext tasks are ineffective against other established techniques. Thus, we theoretically analyze how and when a subsidiary pretext task could be leveraged to assist the goal task of a given DA problem and develop objective subsidiary task suitability criteria. Based on this criteria, we devise a novel process of sticker intervention and cast sticker classification as a supervised subsidiary DA problem concurrent to the goal task unsupervised DA. Our approach not only improves goal task adaptation performance, but also facilitates privacy-oriented source-free DA i.e. without concurrent source-target access. Experiments on the standard Office-31, Office-Home, DomainNet, and VisDA benchmarks demonstrate our superiority for both single-source and multi-source source-free DA. Our approach also complements existing non-source-free works, achieving leading performance.

Multi-source Domain Adaptation (MSDA) is more practical but challenging than the conventional unsupervised domain adaptation due to the involvement of diverse multiple data sources. Two fundamental challenges of MSDA are: (i) how to deal with the diversity in the multiple source domains and (ii) how to cope with the data shift between the target domain and the source domains. In this paper, to address the first challenge, we propose a theoretical-guaranteed approach to combine domain experts locally trained on its own source domain to achieve a combined multi-source teacher that globally predicts well on the mixture of source domains. To address the second challenge, we propose to bridge the gap between the target domain and the mixture of source domains in the latent space via a generator or feature extractor. Together with bridging the gap in the latent space, we train a student to mimic the predictions of the teacher expert on both source and target examples. In addition, our approach is guaranteed with rigorous theory offered insightful justifications of how each component influences the transferring performance. Extensive experiments conducted on three benchmark datasets show that our proposed method achieves state-of-the-art performances to the best of our knowledge.