Neural Networks最新文献_第3页

Multiple interpretation ensemble distillation for graph neural networks 图神经网络的多重解释集合蒸馏。

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-02-03 DOI: 10.1016/j.neunet.2026.108674

Kang Liu , Yuqi Zhang , Shunzhi Yang , Chang-Dong Wang , Yunwen Chen , Xiaowen Ma , Zhenhua Huang

Existing graph knowledge distillation methods suffer from limited absorption of the teacher’s “dark knowledge” because they rely on simple logit alignment, which often causes overfitting or incomplete capture of underlying patterns. Additionally, relying on a single perspective severely restricts the student’s learning effectiveness and generalization ability. To address these issues, we develop a novel Multiple Interpretation Ensemble Distillation (MIED) method. It constructs a multi-interpreter composed of multiple single-layer MLPs for the student, termed the Student Interpretation (SI) component, to interpret knowledge from diversified outputs, thus avoiding representational bias from a single student output. Based on this, it introduces two effective strategies, i.e., Hybrid Sampling and Hierarchical Update. The former employs different sampling strategies for the outputs of the teacher and student (including the SI component). Specifically, the teacher’s output adopts a percentage random sampler, while the outputs of the student and SI component both leverage a positive-negative sampler. With this design, MIED can facilitate better coordination of sample selection and the learning process among the teacher, student, and SI component. The latter updates the parameters of the last layer in the student using the exponential moving average of the fused parameters of the SI component, while the parameters of other layers are updated via a regular optimizer. This enhances the robustness and generalization performance of MIED. Extensive experiments on seven real-world public datasets demonstrate that MIED outperforms existing methods in node classification tasks, resulting in an average improvement of 5.56% over GCN and 27.43% over MLP, respectively. Moreover, compared with directly using multiple students (where the number is consistent with the number of layers in the SI component), MIED achieves improvements approximately 6.00% in time, 50.00% in space, and 0.20% in accuracy. These results indicate that MIED is scalable and generalizable, and exhibits robustness on complex samples.

现有的图知识蒸馏方法对教师“暗知识”的吸收有限，因为它们依赖于简单的logit对齐，这往往会导致对底层模式的过拟合或不完全捕获。此外，依赖单一视角严重制约了学生的学习效果和泛化能力。为了解决这些问题，我们开发了一种新的多重解释集合蒸馏（MIED）方法。它为学生构建了一个由多个单层mlp组成的多解释器，称为学生解释（SI）组件，以解释来自多样化输出的知识，从而避免来自单个学生输出的代表性偏差。在此基础上，介绍了混合采样和分层更新两种有效的策略。前者对教师和学生的输出（包括SI成分）采用不同的抽样策略。具体来说，教师的输出采用百分比随机采样器，而学生和科学探究成分的输出都采用正负采样器。通过这种设计，MIED可以更好地协调教师、学生和科学探究成分之间的样本选择和学习过程。后者使用SI组件的融合参数的指数移动平均来更新学生中最后一层的参数，而其他层的参数则通过常规优化器更新。这提高了MIED的鲁棒性和泛化性能。在7个真实公共数据集上的大量实验表明，MIED在节点分类任务上优于现有方法，比GCN平均提高5.56%，比MLP平均提高27.43%。此外，与直接使用多个学生（人数与SI组件的层数一致）相比，MIED在时间上提高了约6.00%，在空间上提高了50.00%，在精度上提高了0.20%。这些结果表明，MIED具有可扩展性和可泛化性，并且对复杂样本具有鲁棒性。

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引用次数: 0

Multi-Scale pattern-Aware task-Gating network for aerial small object detection 面向空中小目标检测的多尺度模式感知任务门控网络。

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-02-03 DOI: 10.1016/j.neunet.2026.108680

Ben Liang , Yuan Liu , Chao Sui , Yihong Wang , Lin Xiao , Xiubao Sui , Qian Chen

With the advancement of high-precision remote sensing equipment and precision measurement technology, object detection based on remote sensing images (RSIs) has been widely used in military and civilian fields. Different from traditional general-purpose environments, remote sensing presents unique challenges that significantly complicate the detection process. Specifically: (1) RSIs cover extensive monitoring areas, resulting in complex and textured backgrounds; and (2) objects often exhibit cluttered distributions, small sizes, and considerable scale variations across categories. To effectively address these challenges, we propose a Multi-Scale Pattern-Aware Task-Gating Network (MPTNet) for remote sensing object detection. First, we design a Multi-Scale Pattern-Aware Network (MPNet) backbone that employs a small and large kernel convolutional complementary strategy to capture both large-scale and small-scale spatial patterns, yielding more comprehensive semantic features. Next, we introduce a Multi-Head Cross-Space Encoder (MCE) that improves semantic fusion and spatial representation across hierarchical levels. By combining a multi-head mechanism with directional one-dimensional strip convolutions, MCE enhances spatial sensitivity at the pixel level, thus improving object localization in densely textured scenes. To harmonize cross-task synergy, we propose a Dynamic Task-Gating (DTG) head that adaptively recalibrates spatial feature representations between classification and localization branches. Extensive experimental validations on three publicly available datasets, including VisDrone, DIOR, and COCO-mini, demonstrate that our method achieves excellent performance, obtaining AP₅₀ scores of 43.3%, 80.6%, and 49.5%, respectively.

随着高精度遥感设备和精密测量技术的进步，基于遥感图像的目标检测在军事和民用领域得到了广泛的应用。与传统的通用环境不同，遥感带来了独特的挑战，使探测过程变得非常复杂。具体而言：(1)rsi覆盖了广泛的监测区域，导致背景复杂且纹理化；(2)对象通常表现出杂乱的分布，小尺寸，以及跨类别的相当大的规模变化。为了有效地解决这些挑战，我们提出了一种用于遥感目标检测的多尺度模式感知任务门控网络（MPTNet）。首先，我们设计了一个多尺度模式感知网络（MPNet）骨干网，该骨干网采用小型和大型核卷积互补策略来捕获大规模和小规模的空间模式，从而产生更全面的语义特征。接下来，我们介绍了一个多头跨空间编码器（MCE），该编码器改进了跨层次的语义融合和空间表示。MCE通过将多头机制与定向一维条形卷积相结合，提高了像素级的空间灵敏度，从而提高了密集纹理场景中的目标定位。为了协调跨任务协同，我们提出了一种动态任务门控（DTG）头部，该头部可以自适应地重新校准分类和定位分支之间的空间特征表示。在VisDrone、DIOR和COCO-mini三个公开数据集上进行的大量实验验证表明，我们的方法取得了优异的性能，AP50得分分别为43.3%、80.6%和49.5%。

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引用次数: 0

Gradient-informed neural networks: Embedding prior beliefs for learning in low-data scenarios 梯度通知神经网络：嵌入先验信念在低数据场景下的学习。

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-02-02 DOI: 10.1016/j.neunet.2026.108681

Filippo Aglietti , Francesco Della Santa , Andrea Piano , Virginia Aglietti

We propose Gradient-Informed Neural Networks (gradinn s), a methodology that can be used to efficiently approximate a wide range of functions in low-data regimes, when only general prior beliefs are available, a condition that is often encountered in complex engineering problems.

gradinn s incorporate prior beliefs about the first-order derivatives of the target function to constrain the behavior of its gradient, thus implicitly shaping it, without requiring explicit access to the target function’s derivatives. This is achieved by using two Neural Networks: one modeling the target function and a second, auxiliary network expressing the prior beliefs about the first-order derivatives (e.g., smoothness, oscillations, etc.). A customized loss function enables the training of the first network while enforcing gradient constraints derived from the auxiliary network; at the same time, it allows these constraints to be relaxed in accordance with the training data. Numerical experiments demonstrate the advantages of gradinn s, particularly in low-data regimes, with results showing strong performance compared to standard Neural Networks across the tested scenarios, including synthetic benchmark functions and real-world engineering tasks.

我们提出了Gradient-Informed Neural Networks (gradinn s)，这是一种方法，当只有一般先验信念可用时，可以用来在低数据区域有效地近似广泛的函数，这是复杂工程问题中经常遇到的情况。Gradinn s结合了关于目标函数一阶导数的先验信念来约束其梯度的行为，从而隐式地塑造它，而不需要显式地访问目标函数的导数。这是通过使用两个神经网络来实现的：一个建模目标函数，第二个，辅助网络表达关于一阶导数的先验信念（例如，平滑，振荡等）。自定义损失函数能够在训练第一网络的同时强制执行从辅助网络导出的梯度约束；同时，它允许根据训练数据放宽这些约束。数值实验证明了梯度神经网络的优势，特别是在低数据条件下，与标准神经网络相比，在测试场景（包括合成基准函数和现实世界的工程任务）中表现出强大的性能。

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引用次数: 0

Structure-missing graph-level clustering network 缺少结构的图级聚类网络。

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-02-02 DOI: 10.1016/j.neunet.2026.108682

Tianyu Hu , Renda Han , Liu Mao , Jing Chen , Xia Xie

Graph-level clustering aims to group graphs into distinct clusters based on shared structural characteristics or semantic similarities. However, existing graph-level clustering methods generally assume that the input graph structure is complete and overlook the problem of missing relationships that commonly exist in real-world scenarios. These unmodeled missing relationships will lead to the accumulation of structural information distortion during the graph representation learning process, significantly reducing the clustering performance. To this end, we propose a novel method, Structure-Missing Graph-Level Clustering Network (SMGCN), which includes a structure augmentation module LR-SEA, an Anchor Positioning Mechanism, and Joint Contrastive Optimization. Specifically, we first output augmented graphs based on low-rank matrix completion, perform cluster matching using the Hungarian algorithm to obtain anchors, and then force same clustering graphs to converge to the corresponding anchors in the embedding space. According to our research, this is the first time that the graph-level clustering task with missing relations is proposed, and the superiority of our method is demonstrated through experiments on five benchmark datasets, compared with the state-of-the-art methods. Our source codes are available at https://github.com/MrHuSN/SMGCN.

图级聚类旨在基于共享的结构特征或语义相似性将图分组为不同的聚类。然而，现有的图级聚类方法通常假设输入图结构是完整的，忽略了现实场景中普遍存在的关系缺失问题。这些未建模的缺失关系会导致图表示学习过程中结构信息失真的积累，显著降低聚类性能。为此，我们提出了一种新的方法——结构缺失图级聚类网络（SMGCN），该方法包括结构增强模块LR-SEA、锚定位机制和联合对比优化。具体而言，我们首先基于低秩矩阵补全输出增广图，使用匈牙利算法进行聚类匹配以获得锚点，然后强制相同的聚类图收敛到嵌入空间中相应的锚点。根据我们的研究，这是第一次提出具有缺失关系的图级聚类任务，并通过在五个基准数据集上的实验证明了我们的方法与现有方法的优越性。我们的源代码可在https://github.com/MrHuSN/SMGCN上获得。

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引用次数: 0

A knowledge-driven self-supervised learning method for enhancing EEG-based emotion recognition 一种增强基于脑电图的情绪识别的知识驱动自监督学习方法。

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-02-02 DOI: 10.1016/j.neunet.2026.108676

Hanqi Wang , Jingyu Zhang , Peng Ye , Kun Yang , Jichuan Xiong , Xuefeng Liu , Tao Chen , Liang Song

Emotion recognition brain-computer interface (BCI) using electroencephalography (EEG) is crucial for human-computer interaction, medicine, and neuroscience. However, the scarcity of labeled EEG data limits progress in this field. To address this, self-supervised learning has gained attention as a promising approach. Despite its potential, self-supervised methods face two key challenges: (1) ensuring emotion-related information is effectively preserved, as its loss can degrade emotion recognition performance, and (2) overcoming inter-subject variability in EEG signals, which hinders generalization across subjects. To tackle these issues, we propose a novel knowledge-driven self-supervised learning framework for EEG emotion recognition. Our method incorporates domain knowledge to approximate the extraction of statistical feature differential entropy (DE), aiming to preserve emotion-related and generalizable information. The framework consists of two cascaded components as hard and soft alignments: a multi-branch convolutional differential entropy learning (MCDEL) module that simulates the DE extraction process, and a contrastive entropy alignment (CEA) module that exposes complex emotional semantics in high-dimensional space. Experiment results show that our method exhibits superior performance over existing self-supervised methods. The subject-independent mean accuracy and standard deviation of our method reached 84.48% ± 5.79 on SEED and 67.64% ± 6.35 and 68.63% ± 7.77 on the Arousal and Valence dimensions of DREAMER, respectively. We conduct an ablation study to demonstrate the contribution of each proposed component. Moreover, the t-SNE visualization intuitively presents the effect of our method on reducing inter-subject variability and discriminating emotional states.

基于脑电图（EEG）的情绪识别脑机接口（BCI）在人机交互、医学和神经科学等领域具有重要意义。然而，标记脑电图数据的稀缺性限制了这一领域的进展。为了解决这个问题，自我监督学习作为一种有前途的方法得到了关注。尽管具有潜力，但自监督方法面临两个关键挑战：(1)确保情绪相关信息得到有效保存，因为情绪相关信息的丢失会降低情绪识别的性能；(2)克服脑电信号在受试者之间的可变性，这阻碍了受试者之间的泛化。为了解决这些问题，我们提出了一种新的知识驱动的自监督学习框架。该方法结合领域知识来近似提取统计特征微分熵（DE），旨在保留情感相关和可推广的信息。该框架由两个级联组件组成，分别为硬对齐和软对齐：模拟DE提取过程的多分支卷积微分熵学习（MCDEL）模块，以及在高维空间中暴露复杂情感语义的对比熵对齐（CEA）模块。实验结果表明，该方法优于现有的自监督方法。该方法在SEED维度上的平均正确率为84.48% ± 5.79，在dream的唤醒维度和效价维度上的平均正确率为67.64% ± 6.35，标准差为68.63% ± 7.77。我们进行了消融研究，以证明每个提出的组成部分的贡献。此外，t-SNE可视化直观地展示了我们的方法在减少主体间变异性和区分情绪状态方面的效果。

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引用次数: 0

C3Net: A cross-modal collaborative calibration of features for object detection using frames and events C3Net：使用帧和事件进行目标检测的跨模态特征协同校准

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-02-02 DOI: 10.1016/j.neunet.2026.108651

Yunhua Chen , Jinyu Zhong , Yihao Guo , Zequan Xie , Jinsheng Xiao , Pinghua Chen

Object detection by fusing RGB frames and event streams is challenging due to their inherent heterogeneity and significant statistical disparities, which often lead to suboptimal fusion in existing methods. To address this, we introduce C3Net, a novel framework built upon a paradigm shift from direct feature merging to Collaborative Calibration. First, we propose an Adaptive Balancing Time Surface (ABTS) to generate motion-robust event representations by mitigating spatial inconsistencies caused by varying object velocities. Second, the core Cross-Modal Feature Collaborative Calibration Module (CM-FCCM) performs mutual calibration of RGB and event features across channel and spatial dimensions, reducing modality discrepancies before fusion; the calibrated features are then fed back to the respective backbones for enriched feature learning. Finally, an Adaptive Channel Fusion Module (ACFM) dynamically integrates the modalities based on channel-wise confidence. Extensive experiments on PKU-DAVIS-SOD, DSEC-MOD, and PKU-DDD17-CAR datasets demonstrate that C3Net achieves state-of-the-art performance, showcasing its superior ability to leverage the complementary strengths of frames and events.

由于RGB帧和事件流具有固有的异质性和显著的统计差异，因此通过融合RGB帧和事件流进行目标检测具有挑战性，这往往导致现有方法的融合不理想。为了解决这个问题，我们引入了C3Net，这是一个建立在从直接特征合并到协同校准的范式转变之上的新框架。首先，我们提出了一种自适应平衡时间曲面（ABTS），通过减轻由物体速度变化引起的空间不一致性来生成运动鲁棒事件表示。其次，核心跨模态特征协同校准模块（CM-FCCM）跨通道和空间维度对RGB和事件特征进行相互校准，减少融合前的模态差异；然后将校正后的特征反馈到各自的主干进行丰富的特征学习。最后，提出了基于信道置信度的自适应信道融合模块（ACFM）。在PKU-DAVIS-SOD、DSEC-MOD和PKU-DDD17-CAR数据集上进行的大量实验表明，C3Net实现了最先进的性能，展示了其利用框架和事件互补优势的卓越能力。

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引用次数: 0

Improving policy exploitation in online reinforcement learning with instant retrospect action 基于即时回顾的在线强化学习策略开发改进。

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-02-02 DOI: 10.1016/j.neunet.2026.108667

Gong Gao, Weidong Zhao, Xianhui Liu, Ning Jia

Existing value-based online reinforcement learning (RL) algorithms suffer from slow policy exploitation due to ineffective exploration and delayed policy updates. To address these challenges, we propose an algorithm called Instant Retrospect Action (IRA). Specifically, we propose Q-Representation Discrepancy Evolution (RDE) to facilitate Q-network representation learning, enabling discriminative representations for neighboring state-action pairs. In addition, we adopt an explicit method to policy constraints by enabling Greedy Action Guidance (GAG). This is achieved through backtracking historical actions, which effectively enhances the policy update process. Our proposed method relies on providing the learning algorithm with accurate k-nearest-neighbor action value estimates and learning to design a fast-adaptable policy through policy constraints. We further propose the Instant Policy Update (IPU) mechanism, which enhances policy exploitation by systematically increasing the frequency of policy updates. We further discover that the early-stage training conservatism of the IRA method can alleviate the overestimation bias problem in value-based RL. Experimental results show that IRA can significantly improve the learning efficiency and final performance of online RL algorithms on eight MuJoCo continuous control tasks.

现有基于价值的在线强化学习（RL）算法由于探索效率低下和策略更新延迟而存在策略开发缓慢的问题。为了应对这些挑战，我们提出了一种称为即时回顾动作（IRA）的算法。具体来说，我们提出了q -表示差异进化（RDE）来促进q -网络表示学习，使相邻状态-动作对的判别表示成为可能。此外，我们采用了一种显式的方法，通过启用贪婪行为指导（GAG）来实现策略约束。这是通过回溯历史操作实现的，这有效地增强了策略更新过程。我们提出的方法依赖于为学习算法提供准确的k近邻动作值估计，并通过策略约束学习设计快速自适应策略。我们进一步提出了即时政策更新（IPU）机制，该机制通过系统地增加政策更新的频率来增强政策的利用。我们进一步发现，IRA方法的早期训练保守性可以缓解基于值的强化学习中的高估偏差问题。实验结果表明，IRA可以显著提高在线RL算法在8个MuJoCo连续控制任务上的学习效率和最终性能。

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引用次数: 0

Observer-based prescribed-time optimal neural consensus control for six-rotor UAVs: A novel actor-critic reinforcement learning strategy 基于观测器的六旋翼无人机规定时间最优神经共识控制：一种新的actor-critic强化学习策略。

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-02-01 DOI: 10.1016/j.neunet.2026.108644

Yue Zhou , Liang Cao , Yan Lei , Hongru Ren

Six-rotor unmanned aerial vehicles (UAVs) offer significant potential, but still encounter persistent challenges in achieving efficient allocation of limited resources in dynamic and complex environments. Consequently, this paper explores the prescribed-time observer-based optimal consensus control problem for six-rotor UAVs with unified prescribed performance. A practical prescribed-time optimal control scheme is constructed through embedding the prescribed-time control method with a simplified reinforcement learning framework to realize the efficient resource allocation. Leveraging a prescribed-time adjustment function, the novel updating laws for actor and critic neural networks are developed, which guarantee that six-rotor UAVs reach a desired steady state within prescribed time. Moreover, an improved distributed prescribed-time observer is established, ensuring that each follower is able to precisely estimate the velocity and position information of the leader within prescribed time. Then, a series of nonlinear transformations and mappings is proposed, which cannot only satisfy diverse performance requirements under a unified control framework through only adjusting the design parameters a priori but also improve the user-friendliness of implementation and control design. Significantly, the global performance requirement simplifies verification process of initial constraints in traditional performance control methods. Furthermore, an adaptive prescribed-time filter is introduced to address the complexity explosion issue of the backstepping method on six-rotor UAVs, while ensuring the filter error converges within prescribed time. Eventually, simulation results confirm the effectiveness of the designed method.

六旋翼无人机（uav）具有巨大的潜力，但在动态和复杂环境中实现有限资源的有效分配仍然面临着持续的挑战。因此，本文研究了具有统一规定性能的六旋翼无人机的基于规定时间观测器的最优一致控制问题。通过将规定时间控制方法嵌入简化的强化学习框架，构造了一个实用的规定时间最优控制方案，实现了资源的有效配置。利用规定时间的调节函数，提出了新的行动者和评论家神经网络的更新规律，保证了六旋翼无人机在规定时间内达到期望的稳态。建立了改进的分布式规定时间观测器，保证了每个follower都能在规定时间内准确估计leader的速度和位置信息。在此基础上，提出了一系列的非线性变换和映射，不仅可以通过先验地调整设计参数来满足统一控制框架下不同的性能要求，而且提高了实现和控制设计的用户友好性。重要的是，全局性能需求简化了传统性能控制方法中初始约束的验证过程。在保证滤波误差在规定时间内收敛的前提下，引入了自适应规定时间滤波器，解决了六旋翼无人机反步法的复杂度爆炸问题。最后，仿真结果验证了所设计方法的有效性。

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引用次数: 0

DiffMixer: A prediction model based on mixing different frequency features. DiffMixer：一种基于混合不同频率特征的预测模型。

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-02-01 Epub Date: 2025-10-10 DOI: 10.1016/j.neunet.2025.108188

Shengcai Zhang, Huiju Yi, Fanchang Zeng, Xuan Zhang, Zhiying Fu, Dezhi An

Time series forecasting is widely applied in fields such as energy and network security. Various prediction models based on Transformer and MLP architectures have been proposed. However, their performance may decline to varying degrees when applied to real-world sequences with significant non-stationarity. Traditional approaches generally adopt either stabilization or a combination of stabilization and non-stationarity compensation for prediction tasks. However, non-stationarity is a crucial attribute of time series; the former approach tends to eliminate useful non-stationary patterns, while the latter may inadequately capture non-stationary information. Therefore, we propose DiffMixer, which analyzes and predicts different frequencies in non-stationary time series. We use Variational Mode Decomposition (VMD) to obtain multiple frequency components of the sequence, Multi-scale Decomposition (MsD) to optimize the decomposition of downsampled sequences, and Improved Star Aggregate-Redistribute (iSTAR) to capture interdependencies between different frequency components. Additionally, we employ the Frequency domain Processing Block (FPB) to capture global features of different frequency components in the frequency domain, and Dual Dimension Fusion (DuDF) to fuse different frequency components in two dimensions, enhancing the predictive fit for various frequencies. Compared to previous state-of-the-art methods, DiffMixer reduces the Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Symmetric Mean Absolute Percentage Error (SMAPE) by 24.5%, 12.3%, 13.5%, and 6.1%, respectively.

时间序列预测在能源、网络安全等领域有着广泛的应用。各种基于Transformer和MLP架构的预测模型已经被提出。然而，当应用于具有显著非平稳性的现实世界序列时，它们的性能可能会有不同程度的下降。传统方法一般采用镇定或镇定与非平稳性补偿相结合的方法来完成预测任务。然而，非平稳性是时间序列的一个重要属性；前一种方法倾向于消除有用的非平稳模式，而后一种方法可能无法充分捕获非平稳信息。因此，我们提出了DiffMixer来分析和预测非平稳时间序列中的不同频率。我们使用变分模态分解（VMD）来获得序列的多个频率分量，使用多尺度分解（MsD）来优化下采样序列的分解，使用改进的星形聚集-重分布（iSTAR）来捕获不同频率分量之间的相互依赖关系。此外，我们采用频域处理块（FPB）在频域中捕获不同频率分量的全局特征，并采用二维融合（DuDF）在两个维度上融合不同频率分量，增强了对不同频率的预测拟合。与之前最先进的方法相比，DiffMixer将均方误差（MSE）、平均绝对误差（MAE）、均方根误差（RMSE）和对称平均绝对百分比误差（SMAPE）分别降低了24.5%、12.3%、13.5%和6.1%。

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引用次数: 0

Joint generative and alignment adversarial learning for robust incomplete multi-view clustering. 鲁棒不完全多视图聚类的联合生成与对齐对抗学习。

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks

Pub Date : 2026-02-01 Epub Date: 2025-10-03 DOI: 10.1016/j.neunet.2025.108141

Yueyao Li, Bin Wu

Incomplete multi-view clustering (IMVC) has become an area of increasing focus due to the frequent occurrence of missing views in real-world multi-view datasets. Traditional methods often address this by attempting to recover the missing views before clustering. However, these methods face two main limitations: (1) inadequate modeling of cross-view consistency, which weakens the relationships between views, especially with a high missing rate, and (2) limited capacity to generate realistic and diverse missing views, leading to suboptimal clustering results. To tackle these issues, we propose a novel framework, Joint Generative Adversarial Network and Alignment Adversarial (JGA-IMVC). Our framework leverages adversarial learning to simultaneously generate missing views and enforce consistency alignment across views, ensuring effective reconstruction of incomplete data while preserving underlying structural relationships. Extensive experiments on benchmark datasets with varying missing rates demonstrate that JGA-IMVC consistently outperforms current state-of-the-art methods. The model achieves improvements of 3 % to 5 % in key clustering metrics such as Accuracy, Normalized Mutual Information (NMI), and Adjusted Rand Index (ARI). JGA-IMVC excels under high missing conditions, confirming its robustness and generalization capabilities, providing a practical solution for incomplete multi-view clustering scenarios.

不完全多视图聚类（IMVC）已经成为一个日益受到关注的领域，因为在现实世界的多视图数据集中经常出现缺失视图。传统方法通常通过尝试在聚类之前恢复丢失的视图来解决这个问题。然而，这些方法面临两个主要的局限性：(1)对跨视图一致性的建模不足，削弱了视图之间的关系，特别是缺失率高；(2)生成真实多样的缺失视图的能力有限，导致聚类结果不理想。为了解决这些问题，我们提出了一个新的框架，联合生成对抗网络和对齐对抗（JGA-IMVC）。我们的框架利用对抗性学习来同时生成缺失视图并强制视图之间的一致性对齐，确保在保留底层结构关系的同时有效地重建不完整的数据。在具有不同缺失率的基准数据集上进行的大量实验表明，JGA-IMVC始终优于当前最先进的方法。该模型在准确性、标准化互信息（NMI）和调整兰德指数（ARI）等关键聚类指标上实现了3%至5%的改进。JGA-IMVC在高缺失条件下表现出色，证实了其鲁棒性和泛化能力，为不完全多视图聚类场景提供了实用的解决方案。

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