Partial Label Learning (PLL) aims to train multi-class classifiers from examples where each instance is associated with a set of candidate labels, among which the ground-truth label is assumed to be included. While most existing studies assume that partial labels are both instance-independent and reliable, such assumptions often break down in real-world scenarios, where candidate sets may depend on instance-specific features and even exclude the ground-truth label. In this work, we investigate a more realistic setting termed Unreliable Instance-Dependent Partial Label Learning (UIDPLL). To address the challenges in UIDPLL, we propose a novel framework named Neighborhood-guided Label Augmentation and Pruning (NLAP). NLAP exploits the structural consistency among neighboring instances to progressively refine candidate label sets and integrates classifier feedback to disambiguate labels during training. This progressive mechanism improves classification performance by tackling ambiguity caused by noise and instance dependency in partial labels. Furthermore, we provide theoretical guarantees for the proposed NLAP framework, demonstrating that label ambiguity can be effectively reduced through appropriate refinement and pruning procedures. Extensive experiments on both benchmark and real-world datasets demonstrate the robustness and effectiveness of the proposed method.

Label shift, which investigates the adaptation of label distributions between the fixed source and target domains, has attracted significant research interests and broad applications in offline settings. In real-world scenarios, however, data often arrives as a continuous stream. Addressing label shift in online learning settings is paramount. Existing strategies, which tailor traditional offline label shift techniques to online settings, have degraded performance due to the inconsistent estimation of label distributions and violation of convex assumption for theoretical guarantee. In this paper, we propose a novel method to ensure consistent adaptation to online label shift. We construct a new convex risk estimator that is pivotal for both online optimization and theoretical analysis. Furthermore, we enhance an optimistic online algorithm as the base learner and refine the classifier using an ensemble method. Theoretically, we derive a universal dynamic regret which achieves minimax optimal. Extensive experiments on both real-world datasets and human motion task demonstrate the superiority of our method comparing existing methods.

In open-environment applications, data are often collected from heterogeneous modalities with distinct encodings, resulting in feature space heterogeneity. This heterogeneity inherently induces label shift, making cross-modal knowledge transfer particularly challenging when the source and target data exhibit simultaneous heterogeneous feature spaces and shifted label distributions. Existing studies address only partial aspects of this issue, leaving the broader problem unresolved. To bridge this gap, we introduce a new concept of Heterogeneous Label Shift (HLS), targeting this critical but underexplored challenge. We first analyze the impact of heterogeneous feature spaces and label distribution shifts on model generalization and introduce a novel error decomposition theorem. Based on these insights, we propose a bound minimization HLS framework that decouples and tackles feature heterogeneity and label shift accordingly. Extensive experiments on various benchmarks for cross-modal classification validate the effectiveness and practical relevance of the proposed approach.

In many real applications, the data attributes are incremental and the samples are stored with accumulated feature spaces gradually. Although there are several elegant approaches to tackling this problem, the theoretical analysis is still limited. There exist at least two challenges and fundamental questions. 1) How to derive the generalization bounds of these approaches? 2) Under what conditions do these approaches have a strong generalization guarantee? To solve these crucial but rarely studied problems, we provide a comprehensive theoretical analysis in this paper. We begin by summarizing and refining four strategies for addressing feature incremental data. Subsequently, we derive their generalization bounds, providing rigorous and quantitative insights. The theoretical findings highlight the key factors influencing the generalization abilities of different strategies. In tackling the above two fundamental problems, we also provide valuable guidance for exploring other learning challenges in dynamic environments. Finally, the comprehensive experimental and theoretical results mutually validate each other, underscoring the reliability of our conclusions.

Label distribution learning (LDL) has large practical application potentials due to its superiority in dealing with ambiguous label information. Most existing LDL methods are designed in a closed environment, wherein all the elements, e.g., feature and label space, are fixed. Nevertheless, in reality, data are dynamically acquired in the open environment, wherein the feature space can accumulate over time and the label space can be further enriched and refined accordingly with the accumulated feature space. Conducting LDL for such simultaneous augmentation of feature and label is crucial but rarely studied, particularly when the labeled samples with full observations are limited. In this paper, we propose a novel Incremental Label Distribution Learning (ILDL) method to tackle this brand new LDL problem by continuously transiting discriminative information from the previous model to the current one. Concretely, a prior compensation regularization is designed for such discriminative information transitivity. In this manner, the current model has the capacity to reuse the previous model to guide its own training. Furthermore, we present the theoretical analyses about the generalization bound, which provides guarantees for model inheritance. Comprehensive experimental studies validate the effectiveness of our proposal.

Applying current machine learning algorithms in complex and open environments remains challenging, especially when different changing elements are coupled and the training data is scarce. For example, in the activity recognition task, the motion sensors may change position or fall off due to the intensity of the activity, leading to changes in feature space and finally resulting in label noise. Learning from such a problem where the dynamic features are coupled with noisy labels is crucial but rarely studied, particularly when the noisy samples in new feature space are limited. In this paper, we tackle the above problem by proposing a novel two-stage algorithm, called Adaptive Learning for Dynamic features and Noisy labels (ALDN). Specifically, optimal transport is first modified to map the previously learned heterogeneous model to the prior model of the current stage. Then, to fully reuse the mapped prior model, we add a simple yet efficient regularizer as the consistency constraint to assist both the estimation of the noise transition matrix and the model training in the current stage. Finally, two implementations with direct (ALDN-D) and indirect (ALDN-ID) constraints are illustrated for better investigation. More importantly, we provide theoretical guarantees for risk minimization of ALDN-D and ALDN-ID. Extensive experiments validate the effectiveness of the proposed algorithms.

Traditional classification problems assume that features and labels are fixed. However, this assumption is easily violated in open environments. For example, the exponential growth of web pages leads to an expanding feature space with the accumulation of keywords. At the same time, rapid refresh makes it difficult to obtain accurate labels for web pages, often resulting in rough annotations containing potentially correct labels, i.e., partial label set. In such cases, the coupling between the incremental feature space and the partial label set introduces more complex real-world challenges, which deserve attention but have not been fully explored. In this paper, we address this issue by introducing a novel incremental learning approach with Simultaneous Incremental Feature and Partial Label (SIFPL). SIFPL models the data evolution in dynamic and open environments in a two-stage way, consisting of a previous stage and an adapting stage, to deal with the associated challenges. Specifically, to ensure the reusability of the model during adaptation, we impose classifier consistency constraints to enhance the stability of the current model. This constraint leverages historical information from the previous stage to improve the generalization ability of the current model, providing a reliable foundation for further refining the model with new features. Regarding label disambiguation, we filter out incorrect candidate labels based on the principle of minimizing classifier loss, ensuring that the new features and labels effectively support the model's adaptation to the incremental feature space, thereby further refining its performance. Furthermore, we also provide a solid theoretical analysis of the model's generalization bounds, which can validate the efficiency of model inheritance. Experiments on benchmark and real-world datasets validate that the proposed method achieves better accuracy performance than the baseline methods in most cases.

Positive and unlabeled (PU) learning, which trains binary classifiers using only PU data, has gained vast attentions in recent years. Traditional PU learning often assumes that all the positive samples are labeled accurately. Nevertheless, due to the reasons such as sample ambiguity and insufficient algorithms, label noise is almost unavoidable in this scenario. Current PU algorithms neglect the label noise issue in the positive set, which is often biased toward certain instances rather than being uniformly distributed in practical applications. We define this important but understudied problem as PU learning with instance-dependent label noise (PUIDN). To eliminate the adverse impact of IDN, we leverage confidence scores for each instance in the positive set, which establish the connection between samples and labels without any assumption on noise distribution. Then, we propose an unbiased estimator for classification risk considering both label and confidence information, which can be computed immediately from PUIDN data along with their confidence scores. Moreover, our classification framework integrates an optimization strategy of alternating iteration based on the correlation between different confidence information, thereby alleviating the additional requirement for training data. Theoretically, we derive a generalization error bound for our proposed method. Experimentally, the effectiveness of our approach is demonstrated through various types of numerical results.

Needle interventions are crucial in neurosurgery, requiring high precision and stability. This paper presents a 5-DoF head-mounted hydraulic needle robot designed for accurate and targeted needle insertion and neuroimaging in the deep brain. The robot is compact and lightweight by utilizing a hydraulic pipe transmission to connect the needle driver and actuator. The syringe pistons serve as the actuator and executor, enabling synchronized motion, minimal hysteresis, and high-accuracy insertion. The hydraulic transmission system exhibits hysteresis of less than 0.8 mm, with bidirectional insertion accuracy of approximately 0.05 mm. The resulting needle driver features a compact structure measuring 48 mm × 25 mm × 9 mm, accompanied by a 70-mm-long needle guide. The needle driver is mainly 3D printed, while the hydraulic transmission ensures full compatibility with magnetic resonance imaging (MRI) by isolating all electromagnetic parts from the executor. This compact and lightweight robot-assisted needle intervention system significantly enhances the safety, accuracy, and effectiveness of deep-brain neuroimaging. The feasibility of precise positioning and insertion is further demonstrated by deploying an optical coherence tomography (OCT) microneedle in a rat brain.

Miniature rotation actuators have been extensively developed and utilized in optical coherence tomography (OCT) endoscopy, enabling distortion-free OCT imaging in complex and tortuous environments. However, the use of electrical-driven rotation actuators raises safety concerns. Although magnetic-driven rotation actuators have been reported in OCT endoscopy, their use can potentially interfere with other medical devices in clinical settings. Here, we propose a pneumatic miniature rotation actuator that eliminates the electricity and magnetism concerns in circumferential imaging for OCT endoscopy. The rotor of the actuator is designed as a windmill, enabling it to convert air energy into rotation energy. In addition, to maintain the stable rotation, both a sliding bearing with two supporting points and a glass spindle with a half-ball end surface are developed. The rotation speed of our pneumatic actuator can be controlled from 66 to 97 revolutions per second by adjusting the airflow rate from 3.25 to 4.00 liters per minute. By OCT imaging of the human fingers, we demonstrate the feasibility of the pneumatic actuator in electricity-free distal scanning OCT endoscopy. Our pneumatic rotation actuator has wide-ranging potential in various fiber-imaging modalities, including not only OCT but also ultrasound imaging that requires similar rotation capabilities.

With the evolution of data collection ways, label ambiguity has arisen from various applications. How to reduce its uncertainty and leverage its effectiveness is still a challenging task. As two types of representative label ambiguities, Label Distribution Learning (LDL), which annotates each instance with a label distribution, and Emerging New Class (ENC), which focuses on model reusing with new classes, have attached extensive attentions. Nevertheless, in many applications, such as emotion distribution recognition and facial age estimation, we may face a more complicated label ambiguity scenario, i.e., label distribution changing with sample space expanding owing to the new class. To solve this crucial but rarely studied problem, we propose a new framework named as Label Distribution Changing Learning (LDCL) in this paper, together with its theoretical guarantee with generalization error bound. Our approach expands the sample space by re-scaling previous distribution and then estimates the emerging label value via scaling constraint factor. For demonstration, we present two special cases within the framework, together with their optimizations and convergence analyses. Besides evaluating LDCL on most of the existing 13 data sets, we also apply it in the application of emotion distribution recognition. Experimental results demonstrate the effectiveness of our approach in both tackling label ambiguity problem and estimating facial emotion

With the emergence of new data collection ways in many dynamic environment applications, the samples are gathered gradually in the accumulated feature spaces. With the incorporation of new type features, it may result in the augmentation of class numbers. For instance, in activity recognition, using the old features during warm-up, we can separate different warm-up exercises. With the accumulation of new attributes obtained from newly added sensors, we can better separate the newly appeared formal exercises. Learning for such simultaneous augmentation of feature and class is crucial but rarely studied, particularly when the labeled samples with full observations are limited. In this paper, we tackle this problem by proposing a novel incremental learning method for Simultaneous Augmentation of Feature and Class (SAFC) in a two-stage way. To guarantee the reusability of the model trained on previous data, we add a regularizer in the current model, which can provide solid prior in training the new classifier. We also present the theoretical analyses about the generalization bound, which can validate the efficiency of model inheritance. After solving the one-shot problem, we also extend it to multi-shot. Experimental results demonstrate the effectiveness of our approaches, together with their effectiveness in activity recognition applications.

Multiple Instance Learning (MIL) is a widely studied learning paradigm which arises from real applications. Existing MIL methods have achieved prominent performances under the premise of plenty annotation data. Nevertheless, sufficient labeled data is often unattainable due to the high labeling cost. For example, the task in web image identification is to find similar samples among a large size of unlabeled dataset through a small number of provided target pictures. This leads to a particular scenario of Multiple Instance Learning with insufficient Positive and superabundant Unlabeled data (PU-MIL), which is a hot research topic in MIL recently. In this paper, we propose a novel method called Multiple Instance Learning with Bi-level Embedding (MILBLE) to tackle PU-MIL problem. Unlike other PU-MIL method using only simple single-level mapping, the bi-level embedding strategy are designed to customize specific mapping for positive and unlabeled data. It ensures the characteristics of key instance are not erased. Moreover, the weighting measure adopted in positive data can extracts the uncontaminated information of true positive instances without interference from negative ones. Finally, we minimize the classification error loss of mapped examples based on class-prior probability to train the optimal classifier. Experimental results show that our method has better performance than other state-of-the-art methods.

Block‐matching and three‐dimensional filtering (BM3D) is generally considered as a milestone for its outstanding performance in the area of image denoising. However, it still suffers from the loss of image detail due to the utilisation of hard thresholding on transform domain during the phase of the basic estimate. In the frequency domain, a large amount of image detail information is in high frequency, which tends to be mixed with noise. Since its low amplitude is below the threshold, some image detail is filtered out with the noise. To retain more details, this study proposes an improved BM3D. It adopts an adaptable threshold with the core of Gaussian function during hard thresholding, which can filter out more noise while retaining more high‐frequency information. When grouping, the normalised angular distance is taken as a measure of similarity to relieve the interference of noise further and achieve a higher peak signal‐to‐noise ratio (PSNR). The experimental results show that under the background of Gaussian noise with standard deviation of 20–60, the PSNR of denoised images (with a large amount of detail), applied with the authors’ improved algorithm, can be improved by compared with original BM3D.