Neural Networks最新文献

{"title":"Event-triggered decentralized adaptive critic learning control for interconnected systems with nonlinear inequality state constraints","authors":"Wenqian Du ,&nbsp;Mingduo Lin ,&nbsp;Guoling Yuan ,&nbsp;Bo Zhao","doi":"10.1016/j.neunet.2026.108646","DOIUrl":"10.1016/j.neunet.2026.108646","url":null,"abstract":"<div><div>In this paper, an event-triggered decentralized adaptive critic learning (ACL) control method is proposed for interconnected systems with nonlinear inequality state constraints. First, by introducing a slack function, the nonlinear inequality state constraints of original isolated subsystem are transformed into equality forms, and then the original isolated subsystem is augmented to an unconstrained one. Then, by establishing a cost function with discount factors for each isolated subsystem, a local policy iteration-based decentralized control law is developed by solving the Hamilton–Jacobi–Bellman equation with the help of a local critic neural network (NN) for each isolated subsystem. Through developing a novel event-triggering mechanism for each isolated subsystem, the decentralized control policy is updated at the triggering instants only, which assists to save the computational and communication resources. Hereafter, the event-triggered decentralized control law of isolated subsystem is derived. Then, the overall optimal control for the entire interconnected system is derived by constituting an array of developed event-triggered decentralized control laws. Furthermore, the closed-loop nonlinear interconnected system and the weight estimation errors of local critic NNs are guaranteed to be uniformly ultimately bounded. Finally, the effectiveness of the proposed method is validated through two comparative simulation examples.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108646"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HP-GAN: Harnessing pretrained networks for GAN improvement with FakeTwins and discriminator consistency","authors":"Geonhui Son ,&nbsp;Jeong Ryong Lee ,&nbsp;Dosik Hwang","doi":"10.1016/j.neunet.2026.108666","DOIUrl":"10.1016/j.neunet.2026.108666","url":null,"abstract":"<div><div>Generative Adversarial Networks (GANs) have made significant progress in enhancing the quality of image synthesis. Recent methods frequently leverage pretrained networks to calculate perceptual losses or utilize pretrained feature spaces. In this paper, we extend the capabilities of pretrained networks by incorporating innovative self-supervised learning techniques and enforcing consistency between discriminators during GAN training. Our proposed method, named HP-GAN, effectively exploits neural network priors through two primary strategies: FakeTwins and discriminator consistency. FakeTwins leverages pretrained networks as encoders to compute a self-supervised loss and applies this through the generated images to train the generator, thereby enabling the generation of more diverse and high quality images. Additionally, we introduce a consistency mechanism between discriminators that evaluate feature maps extracted from Convolutional Neural Network (CNN) and Vision Transformer (ViT) feature networks. Discriminator consistency promotes coherent learning among discriminators and enhances training robustness by aligning their assessments of image quality. Our extensive evaluation across seventeen datasets-including scenarios with large, small, and limited data, and covering a variety of image domains-demonstrates that HP-GAN consistently outperforms current state-of-the-art methods in terms of Fréchet Inception Distance (FID), achieving significant improvements in image diversity and quality. Code is available at: <span><span>https://github.com/higun2/HP-GAN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108666"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146114793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing adversarial transferability via curvature-aware penalization","authors":"Cheng Peng ,&nbsp;Zeze Tao ,&nbsp;Junyu Liu ,&nbsp;Jinjia Peng","doi":"10.1016/j.neunet.2026.108665","DOIUrl":"10.1016/j.neunet.2026.108665","url":null,"abstract":"<div><div>Transfer-based attack generates adversarial examples on a surrogate model and exploits the intriguing property of transferability to deceive other unknown models, making it practical for real-world scenarios. Recent research has sought to optimize the loss surface by minimizing its maximum loss, which in practice cannot be computed exactly and is instead approximated through gradient ascent. However, the loss landscape becomes increasingly non-linear during later attack stages, making the gradient ascent less effective. To address this challenge, we propose a novel attack called Curvature-Aware Penalization (CAP), which incorporates the gradient norm and the curvature-aware term as regularization terms to maintain the flatness of the loss surface. Since directly computing the Hessian matrix is computationally expensive, we utilize the finite difference method to reduce computational complexity. Specifically, we randomly sample an example from the neighborhood and interpolate gradients at three neighboring points along the example’s gradient direction to approximate the Hessian. Additionally, to reduce the variance caused by random sampling, the combined gradients are averaged over multiple stochastic samples. Comprehensive experimental results demonstrate that our CAP can not only craft adversarial examples with enhanced transferability across various network architectures but also exhibit stronger resistance to state-of-the-art adversarial defense methods. Code is available at <span><span>https://github.com/PC614/CAP</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108665"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146127075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DiffMCG: A diffusion model with mask-conditioned guiding module for medical image classification","authors":"Chen Guan ,&nbsp;Haihong Ai ,&nbsp;Weiwei Wang ,&nbsp;Ravi P. Singh ,&nbsp;Shiya Song","doi":"10.1016/j.neunet.2026.108690","DOIUrl":"10.1016/j.neunet.2026.108690","url":null,"abstract":"<div><div>Diffusion models have application potential in medical image classification tasks due to their effectiveness in eliminating unexpected noise and perturbations from medical images. However, existing diffusion models for medical image classification utilize image features as the condition guiding diffusion model denoising, neglecting the most critical structured semantic information within medical images—namely, the mask of the lesion region. This results in suboptimal denoising performance from diffusion models, consequently impairing classification performance. To address this issue, we propose a diffusion model with the mask-conditioned guiding module called DiffMCG. Specifically, we introduce the Mask-Conditioned Guiding (MCG) module that concurrently extracts features from the medical image and its corresponding mask. Secondly, we design a U-Net denoising network based on the multi-layer perceptron (MLP) that is tailored for low-dimensional vector data and performs denoising tasks within the category label space. Furthermore, we introduce the MMD regularization constraint loss to establish a distributional relationship between the image prediction distribution, mask prediction distribution, and ground-truth label distribution within the label prediction space. This ensures the consistency of multimodal information during the diffusion process. Through analysis of comparative and ablation experiments, we validate the advantages of the MCG module in medical image classification, providing technical support for precision medical diagnostics.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108690"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146167526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Matrix-Transformation based Low-Rank Adaptation (MTLoRA): A brain-Inspired method for parameter-Efficient fine-Tuning","authors":"Yao Liang ,&nbsp;Yuwei Wang ,&nbsp;Yang Li ,&nbsp;Yi Zeng","doi":"10.1016/j.neunet.2026.108642","DOIUrl":"10.1016/j.neunet.2026.108642","url":null,"abstract":"<div><div>Parameter-efficient fine-tuning (PEFT) reduces the compute and memory demands of adapting large language models, yet standard low-rank adapters (e.g., LoRA) can lag full fine-tuning in performance and stability because they restrict updates to a fixed rank-<em>r</em> subspace. We propose Matrix-Transformation based Low-Rank Adaptation (MTLoRA), a brain-inspired extension that inserts a learnable <em>r</em> × <em>r</em> transformation <em>T</em> into the low-rank update (<span><math><mrow><mstyle><mi>Δ</mi></mstyle><mi>W</mi><mo>=</mo><mi>B</mi><mi>T</mi><mi>A</mi></mrow></math></span>). By endowing the subspace with data-adapted geometry (e.g., rotations, scalings, and shears), MTLoRA reparameterizes the rank-<em>r</em> hypothesis class, improving its conditioning and inductive bias at negligible <em>O</em>(<em>r</em>²) overhead, and recovers LoRA when <span><math><mrow><mi>T</mi><mo>=</mo><msub><mi>I</mi><mi>r</mi></msub></mrow></math></span>. We instantiate four structures for <em>T</em>—SHIM <span><math><mrow><mo>(</mo><mi>T</mi><mo>=</mo><mi>C</mi><mo>)</mo></mrow></math></span>, ICFM <span><math><mrow><mo>(</mo><mi>T</mi><mo>=</mo><mi>C</mi><msup><mi>C</mi><mi>⊤</mi></msup><mo>)</mo></mrow></math></span>, CTCM <span><math><mrow><mo>(</mo><mi>T</mi><mo>=</mo><mi>C</mi><mi>D</mi><mo>)</mo></mrow></math></span>, and DTSM <span><math><mrow><mo>(</mo><mi>T</mi><mo>=</mo><mi>C</mi><mo>+</mo><mi>D</mi><mo>)</mo></mrow></math></span>—providing complementary inductive biases (change of basis, PSD metric, staged mixing, dual superposition). An optimization analysis shows that <em>T</em> acts as a learned preconditioner within the subspace, yielding spectral-norm step-size bounds and operator-norm variance contraction that stabilize training. Empirically, MTLoRA delivers consistent gains while preserving PEFT efficiency: on GLUE (General Language Understanding Evaluation) with DeBERTaV3-base, MTLoRA improves the average over LoRA by (+2.0) points (86.9 → 88.9) and matches AdaLoRA (88.9) without any pruning schedule; on natural language generation with GPT-2 Medium, it raises BLEU on DART by (+0.95) and on WebNLG by (+0.56); and in multimodal instruction tuning with LLaVA-1.5-7B, DTSM attains the best average (69.91) with  ∼ 4.7% trainable parameters, outperforming full fine-tuning and strong PEFT baselines. These results indicate that learning geometry inside the low-rank subspace improves both effectiveness and stability, making MTLoRA a practical, plug-compatible alternative to LoRA for large-model fine-tuning.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108642"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-timescale representation with adaptive routing for deep tabular learning under temporal shift","authors":"Tianyu Wang ,&nbsp;Maite Zhang ,&nbsp;Mingxuan Lu ,&nbsp;Mian Li","doi":"10.1016/j.neunet.2026.108670","DOIUrl":"10.1016/j.neunet.2026.108670","url":null,"abstract":"<div><div>In real-world applications, tabular datasets often evolve over time, leading to temporal shift that degrades the long-range neural network performance. Most existing temporal encoding or adaptation solutions treat time cues as fixed auxiliary variables at a single scale. Motivated by the multi-horizon nature of temporal shifts with heterogeneous temporal dynamics, this paper presents TARS (Temporal Abstraction with Routed Scales), a novel plug-and-play method for robust tabular learning under temporal shift, applicable to various deep learning model backbones. First, an explicit temporal encoder decomposes timestamps into short-term recency, mid-term periodicity, and long-term contextual embeddings with structured memory. Next, an implicit drift encoder tracks higher-order distributional statistics at the same aligned timescales, producing drift signals that reflect ongoing temporal dynamics. These signals drive a drift-aware routing mechanism that adaptively weights the explicit temporal pathways, emphasizing the most relevant timescales under current conditions. Finally, a feature-temporal fusion layer integrates the routed temporal representation with original features, injecting context-aware bias. Extensive experiments on eight real-world datasets from the TabReD benchmark show that TARS consistently outperforms the competitive compared methods across various backbone models, achieving up to +2.38% average relative improvement on MLP, +4.08% on DCNv2, etc. Ablation studies verify the complementary contributions of all four modules. These results highlight the effectiveness of TARS for improving the temporal robustness of existing deep tabular models.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108670"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146120930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiple interpretation ensemble distillation for graph neural networks","authors":"Kang Liu ,&nbsp;Yuqi Zhang ,&nbsp;Shunzhi Yang ,&nbsp;Chang-Dong Wang ,&nbsp;Yunwen Chen ,&nbsp;Xiaowen Ma ,&nbsp;Zhenhua Huang","doi":"10.1016/j.neunet.2026.108674","DOIUrl":"10.1016/j.neunet.2026.108674","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108674"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146167504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A two-level neurodynamic approach for heterogeneous networked game under event-triggered quantized mechanism","authors":"Yiyao Xu ,&nbsp;Mengxin Wang ,&nbsp;Ruoyu Yuan ,&nbsp;Sitian Qin","doi":"10.1016/j.neunet.2026.108671","DOIUrl":"10.1016/j.neunet.2026.108671","url":null,"abstract":"<div><div>Game theory provides important theoretical and methodological support for the application expansion of deep learning in complex interaction scenarios, multi-player systems and other aspects. Networked game as a key problem has been investigated in this paper. Since it is inevitable for neighbor players to communicate with each other, key challenges in practical applications is to reduce the communication cost and improve the convergence rate. Also, as the fast development of cyber physical system, players need to obey identical intrinsic dynamics. Hence, in this paper, the communication channel is equipped with one-to-one gradient-based event trigger and logarithmic quantizer, which effectively alleviate the communication burden and reduce communication frequency. Moreover, the passivity-based strategies is used to compensate the lack of complete information, while a piecewise time-varying function is introduced to ensure prescribed time convergence. Besides, proper control input is designed for heterogeneous dynamics players to track Nash equilibrium(NE). It is proven by Lyapunov method that the two-level neurodynamic owns convergence within adjustable time. Additionally, Zeno behavior is excluded. Finally, a numerical example of connectivity control problem for autonomous mobile robots is provided to demonstrate the effectiveness of the proposed neurodynamic approach.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108671"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interactive axial feature selection network for medical image classification","authors":"Shuai Pang,&nbsp;Chunhua Hu,&nbsp;Juan Zhao,&nbsp;Haifang Yu","doi":"10.1016/j.neunet.2026.108661","DOIUrl":"10.1016/j.neunet.2026.108661","url":null,"abstract":"<div><div>To address the differences and correlations between features, as well as to fully utilize the importance of salient semantics in medical image classification tasks, this paper proposes an Interactive Axial Feature Selection Network (IAFSNet), aimed at improving feature representation, effectively filtering noise during classification, thereby enhancing classification performance. The paper introduces a newly designed Feature Interaction Module (FIM), which learns spatial differences between various features and enhances the interdependence and complementarity between local spatial features and global contextual semantics. Additionally, the paper implements a novel Axial Feature Selection Module (AFSM), which filters salient feature semantics from three perspectives: horizontal, vertical, and spatial. By adjusting thresholds, salient features are emphasized while irrelevant noise is eliminated, allowing these key features to cross-aggregate layer by layer and establish interactions among them, ultimately improving classification accuracy. Experimental results on four benchmark datasets demonstrate that the proposed IAFSNet exhibits excellent classification performance and robustness, significantly outperforming many existing classification methods.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108661"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146114790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distilling structural knowledge from CNNs to vision transformers for data-efficient visual recognition","authors":"Dingyao Chen ,&nbsp;Xiao Teng ,&nbsp;Xingyu Shen ,&nbsp;Xun Yang ,&nbsp;Long Lan","doi":"10.1016/j.neunet.2026.108601","DOIUrl":"10.1016/j.neunet.2026.108601","url":null,"abstract":"<div><div>Knowledge distillation (KD) is an effective strategy to transfer learned representations from a pre-trained <span>teacher</span> model to a smaller <span>student</span> model. Current methods for knowledge transfer from convolutional neural networks (CNNs) to vision transformers (ViTs) mainly align output logits. However, such approaches often overlook the rich semantic structures encoded in CNN features, thereby restricting ViTs from effectively inheriting the inductive biases inherent in convolutional architectures. To this end, this paper proposes a <strong>F</strong>eature-based CNN-to-ViT <strong>S</strong>tructural <strong>K</strong>nowledge <strong>D</strong>istillation framework, dubbed <strong>FSKD</strong>, which combines the semantic structural knowledge embedded in CNN (<span>teacher</span>) features with the strength of ViT (<span>student</span>) in capturing long-range dependencies. Specifically, this framework includes a feature alignment module to bridge the representational gap between CNN and ViT features, and it incorporates a global feature alignment loss. Additionally, we develop patch-wise and attention-wise distillation losses to transfer inter-patch similarity and attention distribution, facilitating semantic structural knowledge transfer from CNNs to ViTs. Experimental results demonstrate that the proposed method considerably enhances ViT performance in visual recognition tasks, particularly under scenarios with limited data. Code is available at <span><span>Github</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108601"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}