In the present environment where privacy protection is increasingly emphasized, source-free unsupervised domain adaptation (SFUDA) has garnered more attention compared to standard unsupervised domain adaptation (UDA). It concentrates on transferring knowledge directly from well-trained source models to unlabeled target domains without requiring the involvement of source domain like UDA, greatly enhancing data protection capabilities. Many existing methods employ pseudo-labeling to guide this process, but due to domain shift, pseudo-labels often introduce significant noise. Although there are methods to filter out this noise and mitigate its impact, they may also result in the loss of crucial sample knowledge, leading to performance deterioration. In contrast, we propose a novel approach called Progressive Curriculum Learning with Teacher-Student Collaboration (PCTSC) method to mitigate the adverse influence of noisy labels in SFUDA. Inspired by curriculum learning, PCTSC assesses samples’ learning difficulty and trains models in an incremental manner from easy to hard, thereby enhancing the capability of model to against noise. Furthermore, PCTSC employs a two-stage learning approach: initially, a teacher model directs the student model, and later, the student model transitions to independent learning. We assess the effectiveness of PCTSC by conducting extensive experiments across three benchmark datasets, demonstrating its robustness against pseudo-label noise in SFUDA setting.

As an emerging direction of machine learning, multi-target domain adaptation (MTDA) aims to address the challenges of adapting models to multiple target domains. However, existing studies often focus on single-target domain adaptation or fail to delve into the complexities associated with multiple target domains. So there is a notable lack of comprehensive research and exploration in MTDA. Consequently, we propose a cross-attention with conditional matching for MTDA that intends to overcome the challenges posed by domain discrepancy, multi-target domain heterogeneity, and scalability. Foremost, we design a novel multi-target conditional matching that aims to align the sample distribution by leveraging nearest neighbor principle. This strategy takes into account the unique characteristics of each target domain, facilitating adaptive adaptation across multiple domains. Furthermore, we use the transformer module and well-design a cross-attention mechanism to facilitate the alignment of distributions across the source and target domains, as well as among the target domains, thus mitigating discrepancies among multiple domains. Through integrating the cross-attention mechanism into the training phase, attaining effective alignment of cross-domain distributions, we improve the adaptability and performance of the method. By the end, our approach demonstrates effective and superior experimental results indicating the significance of our work.

The goal of unsupervised domain adaptation person re-identification (UDA Reid) is to achieve feature space alignment between the source domain and the target domain, so that the Reid model can effectively match pedestrians in the target domain. Creating the transitional domain is an effective approach, but existing models often have difficulty synthesizing transitional domains with sufficiently public features. To tackle this challenge, we propose an innovative approach named feature fusion transitional domain (F2TD-Reid), which comprises two essential components: the dictionary fusion module (DFM) and the transitional domain attention module (TDAM). Among them, the DFM utilizes a feature fusion to extract and reconstruct pedestrian images from instances, focusing on capturing the essential visual elements within the images. For the TDAM, it further refines the feature extraction of instance points through an innovative weighted attention mechanism. These two modules optimize the generation process of scaling factors, thereby facilitating the transfer of knowledge between the source domain and the target domain. Through a series of comparative experiments, we verify the superiority of the F2TD-Reid method in solving UDA Reid. The code is available at https://github.com/1x-x/F2TD-Reid.

Multi-view learning is a machine learning paradigm that utilizes multiple feature sets or data sources to improve learning performance and generalization. However, existing multi-view learning methods often do not capture and utilize information from different views very well, especially when the relationships between views are complex and of varying quality. In this paper, we propose a novel multi-view learning framework for the multi-view classification task, called Gated Cross-Correlation Network (GCCNet), which addresses these challenges by integrating the three key operational levels in multi-view learning: representation, fusion, and decision. Specifically, GCCNet contains a novel component called the Multi-View Gated Information Distributor (MVGID) to enhance noise filtering and optimize the retention of critical information. In addition, GCCNet uses cross-correlation analysis to reveal dependencies and interactions between different views, as well as integrates an adaptive weighted joint decision strategy to mitigate the interference of low-quality views. Thus, GCCNet can not only comprehensively capture and utilize information from different views, but also facilitate information exchange and synergy between views, ultimately improving the overall performance of the model. Extensive experimental results on ten benchmark datasets show GCCNet's outperforms state-of-the-art methods on eight out of ten datasets, validating its effectiveness and superiority in multi-view learning.

Open-set domain adaptation (OSDA) seeks to transfer knowledge from a labeled source domain to an unlabeled target domain containing novel classes. Traditional OSDA methods rarely account for the uncertainty in predictions and typically require additional training overhead. Evidential deep learning (EDL) transforms the model's predictions from point estimates to distributions over the probability simplex by replacing the standard softmax output of classification neural networks with Dirichlet distributions. Considering the presence of out-of-distribution novel classes in OSDA and the additional overhead of existing methods, we propose EDL for open-set active domain adaptation (EOSADA). Leveraging EDL, we construct an open-set classifier and employ a two-round selection strategy guided by the data uncertainty of target domain samples and semantic similarity scores with known classes. This strategy balances the selection of samples from known and novel classes while identifying informative samples, thereby maximizing the performance of the model in OSDA scenarios without modifying the model structure and utilizing a limited annotation budget. Extensive experiments demonstrate the superiority of our approach.

Source-free unsupervised domain adaptation (SFUDA) aims to conduct prediction on the target domain by leveraging knowledge from the well-trained source model. Due to the absence of source data in the SFUDA setting, the existing methods mainly build the target classifier by fine-tuning the source model incorporated with empirical adaptation losses. Although these methods have achieved somewhat promising results, nearly all of them typically suffer from the closed-fitting dilemma that their models are dominantly affected by these easy-to-distinguish instances than those hard-to-distinguish ones, resulting from the absence of the labeled source data. To address aforementioned issues, we propose the Dipolar Confidence Learning (DCL) for SFUDA. Specifically, we conduct positive confidence learning on the samples with standard outputs to avoid overfitting of the model to these samples. In contrast, we perform negative confidence learning for the samples with abnormal outputs to optimize the complementary label, which forces the network to pay more attention to these confusing samples. Furthermore, to achieve more generalized domain alignment, both the confidence-based fuzzy mixup and rotation-based self-supervised learning are respectively constructed to boost the representation ability of the target model. Finally, extensive experiments are conducted to demonstrate the effectiveness and performance superiority of the proposed method.

Unsupervised domain adaptation (UDA) is an emerging learning paradigm that models on unlabeled datasets by leveraging model knowledge built on other labeled datasets, in which the statistical distributions of these datasets are usually not identical. Formally, UDA is to leverage knowledge from a labeled source domain to promote an unlabeled target domain. Although there have been a variety of methods proposed to address the UDA problem, most of them are dedicated to single-source-to-single-target domain, while the works on single-source-to-multitarget domain are relatively rare. Compared to the single-source domain with single-target domain scenario, the UDA from single-source domain to multitarget domain is more challenging since it needs to consider not only the relationships between the source and the target domains but also those among the target domains. To this end, this article proposes a kind of dictionary learning-based unsupervised multitarget domain adaptation method (DL-UMTDA). In DL-UMTDA, a common dictionary is constructed to correlate the single-source and multitarget domains, while individual dictionaries are designed to exploit the private knowledge for the target domains. Through learning the corresponding dictionary representation coefficients in the UDA process, the correlations from the source to the target domains as well as these potential relationships between the target domains can be effectively exploited. In addition, we design an alternating algorithm to solve the DL-UMTDA model with theoretical convergence guarantee. Finally, extensive experiments on benchmark (Office + Caltech) and real datasets (AgeDB, Morph, and CACD) validate the superiority of the proposed method.

In spite of unique advantages like higher recognition accuracy and better generalization capability, automatic modulation classification (AMC)-oriented deep neural networks (ADNNs) are still vulnerable to adversarial examples (AEs). Recent results revealed that an attacker can easily fool ADNNs through adding a small and imperceptible perturbation to the original signal. Among different AE generation methods, universal adversarial perturbation (UAP) has unique characteristics, including input agnostic and shift invariance. However, applying UAP directly to RF signals faces three main challenges, i.e., perturbation neutralization, high perceptibility, and dependency of original signals. In this backdrop, a novel UAP under frequency and data constraints (UAP-FD) attack is put forward for solving these problems in this article. First, an individual perturbation is filtered based on the representation visualization algorithm to counter the neutralization problem in perturbation integration. Second, the high-frequency components in the integrated UAP is eliminated through signal decomposition and reconstruction for promoting the imperceptibility. Third, a proxy signal generation method is proposed to help UAP-FD adapt to data-free black-box settings. A series of experiments is conducted to evaluate the aggressiveness and imperceptibility of UAP-FD attack in different settings on a public data set. Results show that, compared with the existing proposal, UAP-FD has a 40% higher fooling rate, and it can reduce the accuracy of the ADNN model from 83% to 9% while maintaining a good imperceptibility and shift-invariance property. In addition, UAP-FD is applied to real-world captured signals over the transmission channel; and it can reduce the model accuracy from 98.3% to 12.5%.

Source-free unsupervised domain adaptation (SFUDA) aims to accomplish the task of adaptation to the target domain by utilizing pre-trained source domain model and unlabeled target domain samples, without directly accessing any source domain data. Although many SFUDA works use the pseudo-labeling strategy to improve the accuracy of pseudo-labels in the target domain, these strategies ignore the influence of domain shift on calculating the reference distribution of pseudo-labels. In this article, we propose a novel kind of SFUDA with trusted pseudo samples (SFUDA-TPS), which uses reliable feature reference distribution to solve the SFUDA problem. In SFUDA-TPS, we design a target feature correcting classifier to alleviate the problem of feature reference distribution deviating from target domain samples distribution. On this basis, the more reliable feature reference distribution is calculated by selecting the target domain samples with a high amount of information, i.e., low entropy in the fixed source domain classifier and target feature correcting classifier. The implicit alignment between the source domain and target domain is realized by learning the source domain distributions hidden in the fixed source domain classifier. Experimental evaluations illustrate the effectiveness of our proposed method in solving SFUDA tasks.

In unsupervised domain adaptation (UDA), a target-domain model is trained by the supervised knowledge from a source domain. Although UDA has recently received much attention, most existing UDA methods have ignored the alignment in label space while mostly concentrating on alignment of feature space. Even worse, they have payed less attention to the dynamic relationship between domain alignment and discrimination, leading to degenerated performance. In this work, we propose a new kind of UDA through aligning in both the feature and label spaces (DAFL), in which a dynamic weight is designed and deployed between domain alignment and discrimination enhancement according to their conditions. Specifically, the cross-domain distribution divergence is reduced by the weighted class-level feature space alignment as well as the compacted and discriminative label space alignment. Furthermore, the balancing weight between adaptation alignment and discrimination enhancement is dynamically adjusted to regularize the adversarial domain adaptation. Then, the generalization ability of the DAFL model is enhanced by adding discrepant classification with theoretical analysis. Finally, extensive experiments validate effectiveness and superiority of the proposed approach.

Canonical correlation analysis (CCA) is a typical statistical model used to analyze the correlation components between different view representations of the same objects. When the label information is available with the data representations, CCA can be extended to its discriminative counterparts by incorporating supervision in the analysis. Although most discriminative variants of CCA have achieved improved results, nearly all of their objective functions are nonconvex, implying that optimal solutions are difficult to obtain. More important, that cross-view representations from the same sample should be consistent, that is, the cross-view semantic consistency has however not been modeled. To overcome these drawbacks, in this article, we propose a discriminant semantic correlation analysis (DSCA) model by modeling the cross-view semantic consistency for each object in the sample space rather than in the commonly used feature space. To boost the nonlinear discriminating capability of DSCA, we extend it from the Euclidean to the geodesic space by transforming the metric and incorporating both the cross-view semantic and representation correlation information and consequently obtain our final model with convex objective, namely, convex DSCA (C-DSCA). Finally, with extensive experiments and comparisons, we validate the effectiveness and superiority of the proposed method.

Domain adaptation (DA) aims at facilitating the target model training by leveraging knowledge from related but distribution-inconsistent source domain. Most of the previous DA works concentrate on homogeneous scenarios, where the source and target domains are assumed to share the same feature space. Nevertheless, frequently, in reality, the domains are not consistent in not only data distribution but also the representation space and feature dimensions. That is, these domains are heterogeneous. Although many works have attempted to handle such heterogeneous DA (HDA) by transforming HDA to homogeneous counterparts or performing DA jointly with domain transformation, nearly all of them just concentrate on the feature and distribution alignment across domains, neglecting the structure and classification space preservation for domains themselves. In this work, we propose a novel HDA model, namely, heterogeneous classification space alignment (HCSA), which leverages knowledge from both the source samples and model parameters to the target. In HCSA, structure preservation, distribution, and classification space alignment are implemented, jointly with feature representation by transferring both the source-domain representation and model knowledge. Moreover, we design an alternating algorithm to optimize the HCSA model with guaranteed convergence and complexity analysis. In addition, the HCSA model is further extended with deep network architecture. Finally, we experimentally evaluate the effectiveness of the proposed method by showing its superiority to the compared approaches.

Due to emerging development of intelligent sensing technologies in Internet of Things, multisensor cooperation has been widely deployed in applications. Although multisensor information fusion can be addressed by multiview learning, its performance tends to degrade if any one sensor is disturbed with annoying noises by the environment or other factors. Therefore, fusing these cross-sensor data in a reliable and secure manner while removing those noises is crucial. Although there have been outlier-against multiview works proposed, most of them suffer from redundant parameters or performance degradation. Even worse, few of them have considered the complementary information across the sensors. In this article, we argue that in multiview information fusion, not only the clean data, but also those outliers share the same prototypes in a common space, except that the outliers are disturbed with noises. To this end, we propose a type of robust multiview prototype (RMVP) learning to fuse the sensing data while removing the noises automatically in the learning process. Specifically, in RMVP, projection matrices are designed for each sensing view to sketch the data prototypes. In addition, one auxiliary margin matrix is modeled for each sensing view to capture its data noises through penalizing a sparsity regularization on it. Afterwards, an alternating algorithm is presented to solve the proposed model. Finally, extensive experiments on intelligent sensing data sets are conducted to testify the effectiveness of the proposed method.

Although the least-squares regression (LSR) has achieved great success in regression tasks, its discriminating ability is limited since the margins between classes are not specially preserved. To mitigate this issue, dragging techniques have been introduced to remodel the regression targets of LSR. Such variants have gained certain performance improvement, but their generalization ability is still unsatisfactory when handling real data. This is because structure-related information, which is typically contained in the data, is not exploited. To overcome this shortcoming, in this article, we construct a multioutput regression model by exploiting the intraclass correlations and input-output relationships via a structure matrix. We also discriminatively enlarge the regression margins by embedding a metric that is guided automatically by the training data. To better handle such structured data with ordinal labels, we encode the model output as cumulative attributes and, hence, obtain our proposed model, termed structure-exploiting discriminative ordinal multioutput regression (SEDOMOR). In addition, to further enhance its distinguishing ability, we extend the SEDOMOR to its nonlinear counterparts with kernel functions and deep architectures. We also derive the corresponding optimization algorithms for solving these models and prove their convergence. Finally, extensive experiments have testified the effectiveness and superiority of the proposed methods.

Canonical correlation analysis (CCA) is a typical and useful learning paradigm in big data analysis for capturing correlation across multiple views of the same objects. When dealing with data with additional ordinal information, traditional CCA suffers from poor performance due to ignoring the ordinal relationships within the data. Such data is becoming increasingly common, as either temporal or sequential information is often associated with the data collection process. To incorporate the ordinal information into the objective function of CCA, the so-called ordinal discriminative CCA has been presented in the literature. Although ordinal discriminative CCA can yield better ordinal regression results, its performance deteriorates when data is corrupted with noise and outliers, as it tends to smear the order information contained in class centers. To address this issue, in this article we construct a robust manifold-preserved ordinal discriminative correlation regression (rmODCR). The robustness is achieved by replacing the traditional ( l 2 -norm) class centers with l p -norm centers, where p is efficiently estimated according to the moments of the data distributions, as well as by incorporating the manifold distribution information of the data in the objective optimization. In addition, we further extend the robust manifold-preserved ordinal discriminative correlation regression to deep convolutional architectures. Extensive experimental evaluations have demonstrated the superiority of the proposed methods.