Gaussian Process-Based Transfer Kernel Learning for Unsupervised Domain Adaptation

Domain Adaptation (DA) methods are usually carried out by means of simply reducing the marginal distribution differences between the source and target domains, and subsequently using the resultant trained classifier, namely source classifier, for use in the target domain. However, in many cases, the true predictive distributions of the source and target domains can be vastly different especially when their class distributions are skewed, causing the issues of sample selection bias in DA. Hence, DA methods which leverage the source labeled data may suffer from poor generalization in the target domain, resulting in negative transfer. In addition, we observed that many DA methods use either a source classifier or a linear combination of source classifiers with a fixed weighting for predicting the target unlabeled data. Essentially, the labels of the target unlabeled data are spanned by the prediction of these source classifiers. Motivated by these observations, in this paper, we propose to construct many source classifiers of diverse biases and learn the weight for each source classifier by directly minimizing the structural risk defined on the target unlabeled data so as to heal the possible sample selection bias. Since the weights are learned by maximizing the margin of separation between opposite classes on the target unlabeled data, the proposed method is established here as Maximal Margin Target Label Learning (MMTLL), which is in a form of Multiple Kernel Learning problem with many label kernels. Extensive experimental studies of MMTLL against several state-of-the-art methods on the Sentiment and Newsgroups datasets with various imbalanced class settings showed that MMTLL exhibited robust accuracies on all the settings considered and was resilient to negative transfer, in contrast to other counterpart methods which suffered significantly in prediction accuracy.

Importance-weighted conditional adversarial network for unsupervised domain adaptation

In the absence of the labeled samples in a domain referred to as target domain, Domain Adaptation (DA) techniques come in handy. Generally, DA techniques assume there are available source domains that share similar predictive function with the target domain. Two core challenges of DA typically arise, variance that exists between source and target domains, and the inherent source hypothesis bias. In this paper, we first propose a Stability Transfer criterion for selecting relevant source domains with low variance. With this criterion, we introduce a TARget learning Assisted by Source Classifier Adaptation (TARASCA) method to address the two core challenges that have impeded the performances of DA techniques. To verify the robustness of TARASCA, extensive experimental studies are carried out with comparison to several state-of-the-art DA methods on the real-world Sentiment and Newsgroups datasets, where various settings for the class ratios of the source and target domains are considered.

Joint predictive model and representation learning for visual domain adaptation

All current deep learning-based prediction methods for remaining useful life (RUL) assume that training and testing data have similar distributions, but the existence of various operating conditions, failure modes, and noise lead to insufficient data with similar distributions during the training process, thereby reducing RUL prediction performance. Domain adaptation can effectively solve this problem by learning the cross-domain invariant features of the source domain and target domain to reduce the distribution difference. However, most domain adaptive methods extract the source domain and target domain features into a single space for feature alignment, which may leave out effective information and affect the accuracy of prediction. To address this problem, we propose a data-driven approach named long short-term memory network and multi-representation domain adaptation (LSTM-MRAN). We standardize and process the degraded sensor data with a sliding time window, use LSTM to extract features from the degraded data, and mine the time series information between the data. Then, we use multiple substructures in multi-representation domain adaptation to extract features of the source domain and target domain from different spaces and align features by minimizing conditional maximum mean difference (CMMD) loss functions. The effectiveness of the method is verified by the CMAPSS dataset. Compared with methods without domain adaptation and other transfer learning methods, the proposed method provides more reliable RUL prediction results under datasets with different operating conditions and failure modes.

In real-world hyperspectral image (HSI) classification, the limited annotated examples usually lead to the insufficiently trained classifier, which further generates low classification accuracy. To overcome this challenge, Domain Adaptation (DA) methods have been developed to transfer learnable knowledge from the external HSIs with sufficient labeled examples ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., the source domain) to the interested HSI with scarce labeled examples ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., the target domain). Conventional DA approaches often pseudo-label the examples with high classification confidence and then incorporate them in the training process. However, due to the significant domain gap, relying solely on the confident examples may not be adequate to achieve satisfactory performance. Therefore, this paper proposes an Interactive Easy-to-Hard Domain Adaptation method (IEH-DA) to arrange the adaptation process so that the “easy” examples are adapted ahead of the “hard” ones. In an early stage, the easy examples with high pseudo-labeling confidence are selected for the adversarial learning based DA. In a later stage, the “hard” examples with high informativity are further selected, and they are interactively labeled by human expert to provide accurate supervision information for adaptation. As a result, the examples in target domain are used in an easy-to-hard way, which forms a curriculum sequence for orderly model training. Extensive experiments conducted on typical public datasets demonstrate that IEH-DA outperforms other state-of-the-art DA methods for HSI classification.

Fault diagnosis is very important for condition based maintenance. Recently, deep learning models are introduced to learn hierarchical representations from raw data instead of using hand-crafted features, which exhibit excellent performance. The success of current deep learning lies in: 1) the training (source domain) and testing (target domain) datasets are from the same feature distribution; 2) Enough labeled data with fault information exist. However, because the machine operates under a non-stationary working condition, the trained model built on the source domain can not be directly applied on the target domain. Moreover, since no sufficient labeled or even unlabeled data are available in target domain, collecting the labeled data and building the model from scratch is time-consuming and expensive. Motivated by transfer learning (TL), we present a new fault diagnosis method, which generalizes convolutional neural network (CNN) to TL scenario. Two layers with regard to task-specific features are adapted in a layer-wise way to regularize the parameters of CNN. What's more, the domain loss is calculated by a linear combination of multiple Gaussian kernels so that the ability of adaptation is enhanced compared to single kernel. Through these two means, the distribution discrepancy is reduced and the transferable features are learned. The proposed method is validated by transfer fault diagnosis experiments. Compared to CNN without domain adaptation and shallow transfer learning methods, the proposed method gets the best performance for fault classification.

With the enhancement of air-based and space-based perception capabilities, space-aeronautics incorporation and integration is growing in importance. Full domain awareness is crucial for integrated perception systems, in which domain adaptation is one of the key problems in improving the performance of cross-domain perception. Deep learning is currently an advanced technique for complex inverse synthetic aperture radar (ISAR) object recognition. However, the training procedure needs many annotated samples, which is insufficient for certain targets, such as aircraft. Few-shot learning provides a new approach to solving the above problem by transferring useful knowledge from other domains, such as optical images from satellites. Nevertheless, it fails to fully consider the domain shift between the source and target domains, generally neglecting the transferability of training samples in the learning process. Consequently, it produces suboptimal recognition accuracy. To address the composite problems mentioned above, we propose a domain adaptive few-shot learning method from satellites to an ISAR called S2I-DAFSL for aircraft recognition tasks. Furthermore, unlike conventional domain adaptation methods that directly align the distributions, the attention transferred importance-weighting network (ATIN) is proposed to improve the transferability in the domain adaptation procedure. Compared with state-of-the-art methods, it shows that the proposed method achieves better performance, increasing the accuracy and effectiveness of classification, which is more suitable for cross-domain few-shot ISAR aircraft recognition tasks.

In this paper, we propose a new unsupervised domain adaptation method for document classification. We address the problem of domain adaptation for document classification where the source and target domains do not differ significantly and there is no labeled data in the target domain. In this case, we can use conventional semi-supervised learning. Thus, we use the naive Bayes-based expectation-maximization method (NBEM) which is very effective for document classification. However, NBEM does not utilize the difference between a source domain and a target domain. We combine NBEM with the feature weighted method for domain adaptation, referred to as “self-training feature weight” (STFW). Our proposed method alternately uses NBEM and STFW to gradually improve document classification precision for a target domain. This method significantly outperforms the conventional unsupervised methods for domain adaptation.

A unified framework for visual domain adaptation with covariance matching

Easy Domain Adaptation for cross-subject multi-view emotion recognition

In this paper, we propose a new framework called domain adaptation machine (DAM) for the multiple source domain adaption problem. Under this framework, we learn a robust decision function (referred to as target classifier) for label prediction of instances from the target domain by leveraging a set of base classifiers which are prelearned by using labeled instances either from the source domains or from the source domains and the target domain. With the base classifiers, we propose a new domain-dependent regularizer based on smoothness assumption, which enforces that the target classifier shares similar decision values with the relevant base classifiers on the unlabeled instances from the target domain. This newly proposed regularizer can be readily incorporated into many kernel methods (e.g., support vector machines (SVM), support vector regression, and least-squares SVM (LS-SVM)). For domain adaptation, we also develop two new domain adaptation methods referred to as FastDAM and UniverDAM. In FastDAM, we introduce our proposed domain-dependent regularizer into LS-SVM as well as employ a sparsity regularizer to learn a sparse target classifier with the support vectors only from the target domain, which thus makes the label prediction on any test instance very fast. In UniverDAM, we additionally make use of the instances from the source domains as Universum to further enhance the generalization ability of the target classifier. We evaluate our two methods on the challenging TRECIVD 2005 dataset for the large-scale video concept detection task as well as on the 20 newsgroups and email spam datasets for document retrieval. Comprehensive experiments demonstrate that FastDAM and UniverDAM outperform the existing multiple source domain adaptation methods for the two applications.

Domain adaptation (DA), which leverages labeled data from related source domains, comes in handy when the label information of the target domain is scarce or unavailable. However, as the source data do not come from the same origin as that of the target domain, the predictive distributions of the source and target domains are likely to differ in reality. At the extreme, the predictive distributions of the source domains can differ completely from that of the target domain. In such case, using the learned source classifier to assist in the prediction of target data can result in prediction performance that is poorer than that with the omission of the source data. This phenomenon is established as negative transfer with impact known to be more severe in the multiclass context. To combat negative transfer due to differing predictive distributions across domains, we first introduce the notion of positive transferability for the assessment of synergy between the source and target domains in their prediction models, and we also propose a criterion to measure the positive transferability between sample pairs of different domains in terms of their prediction distributions. With the new measure, a predictive distribution matching (PDM) regularizer and a PDM framework learn the target classifier by favoring source data with large positive transferability while inferring the labels of target unlabeled data. Extensive experiments are conducted to validate the performance efficacy of the proposed PDM framework using several commonly used multidomain benchmark data sets, including Sentiment, Reuters, and Newsgroup, in the context of both binary-class and multiclass domains. Subsequently, the PDM framework is put to work on a real-world scenario pertaining to water cluster molecule identification. The experimental results illustrate the adverse impact of negative transfer on several state-of-the-art DA methods, whereas the proposed framework exhibits excellent and robust predictive performances.

Deep multi-Wasserstein unsupervised domain adaptation

Transfer learning and domain adaptation (DA) methods have been utilized in bearing prognostic and health management, but most of the current DA methods do not take into account the feature scale change of degraded features when aligning the feature distribution, and these methods are more suitable for the classification problem, which is more robust to the feature scale change. However, they perform poorly in regression problems. In addition, most of the remaining useful life (RUL) prediction methods require preprocessing such as statistical feature extraction on the signal, which makes the prediction process complicated. To solve the above problems, a DA method based on the representation subspace distance (RSD) is proposed for predicting the bearing RUL under different operating conditions. First, the proposed convolutional neural network (CNN) self-attention (SA) long short term memory network model is utilized to extract the deep features from the original signal, which overcomes the limitations of the CNN in extracting time series. Then, the RSD in the Riemannian geometry of the Grassmann manifold is proposed as a domain transfer loss to learn domain invariant features. The modified method can align the feature distribution of the source domain and the target domain without changing the feature scale. At the same time, the bases mismatch penalization is introduced to avoid destroying the semantic information of the features in the process of domain alignment. Finally, the effectiveness of the proposed method is verified by experiments on four types of transfer tasks, and its superiority is also demonstrated by comparison with other advanced methods.