Neural Networks最新文献

FSDM: An efficient video super-resolution method based on Frames-Shift Diffusion Model

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-03 DOI: 10.1016/j.neunet.2025.107435

Shijie Yang , Chao Chen , Jie Liu , Jie Tang , Gangshan Wu

{"title":"FSDM: An efficient video super-resolution method based on Frames-Shift Diffusion Model","authors":"Shijie Yang , Chao Chen , Jie Liu , Jie Tang , Gangshan Wu","doi":"10.1016/j.neunet.2025.107435","DOIUrl":"10.1016/j.neunet.2025.107435","url":null,"abstract":"<div><div>Video super-resolution is a fundamental task aimed at enhancing video quality through intricate modeling techniques. Recent advancements in diffusion models have significantly enhanced image super-resolution processing capabilities. However, their integration into video super-resolution workflows remains constrained due to the computational complexity of temporal fusion modules, demanding more computational resources compared to their image counterparts. To address this challenge, we propose a novel approach: a Frames-Shift Diffusion Model based on the image diffusion models. Compared to directly training diffusion-based video super-resolution models, redesigning the diffusion process of image models without introducing complex temporal modules requires minimal training consumption. We incorporate temporal information into the image super-resolution diffusion model by using optical flow and perform multi-frame fusion. This model adapts the diffusion process to smoothly transition from image super-resolution to video super-resolution diffusion without additional weight parameters. As a result, the Frames-Shift Diffusion Model efficiently processes videos frame by frame while maintaining computational efficiency and achieving superior performance. It enhances perceptual quality and achieves comparable performance to other state-of-the-art diffusion-based VSR methods in PSNR and SSIM. This approach optimizes video super-resolution by simplifying the integration of temporal data, thus addressing key challenges in the field.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107435"},"PeriodicalIF":6.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769264","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}

引用次数: 0

Localize-diffusion based dual-branch anomaly detection

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-03 DOI: 10.1016/j.neunet.2025.107439

Jielin Jiang , Xiying Liu , Peiyi Yan , Shun Wei , Yan Cui

{"title":"Localize-diffusion based dual-branch anomaly detection","authors":"Jielin Jiang , Xiying Liu , Peiyi Yan , Shun Wei , Yan Cui","doi":"10.1016/j.neunet.2025.107439","DOIUrl":"10.1016/j.neunet.2025.107439","url":null,"abstract":"<div><div>Due to the scarcity of real anomaly samples for use in anomaly detection studies, data augmentation methods are typically employed to generate pseudo anomaly samples to supplement the limited real samples. However, existing data augmentation methods often generate image patches with fixed shapes as anomalies in random regions. These anomalies are unrealistic and lack diversity, resulting in generated samples with limited practical value. To address this issue, we propose a dual-branch anomaly detection (DBA) technique based on Localize-Diffusion (LD) augmentation. LD can infer the approximate position and size of the object to be detected based on the samples’ color distribution: this can effectively avoid the problem of patch generation outside the target object’s location. LD subsequently incorporates hard augmentation and continuously propagates irregular patches to the surrounding area, which enriches the diversity of the generated samples. Based on the anomalies’ multi-scale characteristics, DBA adopts two branches for training and anomaly detection based on the generated pseudo anomaly samples: one focuses on identifying anomaly-specific features from learned anomalies, while the other discriminates between normal and anomaly samples based on residual features in the latent space. Finally, an adaptive scoring module is used to calculate a weighted average of the results of the two branches, achieving the goal of anomaly detection. Extensive experimental analyses reveal that DBA achieves excellent anomaly detection performance using only 14.2M parameters, notably achieving 99.6 detection AUC on the MVTec AD dataset.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107439"},"PeriodicalIF":6.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769163","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}

引用次数: 0

Source-free time series domain adaptation with wavelet-based multi-scale temporal imputation

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-02 DOI: 10.1016/j.neunet.2025.107428

Yingyi Zhong, Wen’an Zhou, Liwen Tao

{"title":"Source-free time series domain adaptation with wavelet-based multi-scale temporal imputation","authors":"Yingyi Zhong, Wen’an Zhou, Liwen Tao","doi":"10.1016/j.neunet.2025.107428","DOIUrl":"10.1016/j.neunet.2025.107428","url":null,"abstract":"<div><div>Recent works on source-free domain adaptation (SFDA) for time series reveal the effectiveness of learning domain-invariant temporal dynamics on improving the cross-domain performance of the model. However, existing SFDA methods for time series mainly focus on modeling the original sequence, lacking the utilization of the multi-scale properties of time series. This may result in insufficient extraction of domain-invariant temporal patterns. Furthermore, previous multi-scale analysis methods typically ignore important frequency domain information during multi-scale division, leading to the limited ability for multi-scale time series modeling. To this end, we propose LEMON, a novel SFDA method for time series with wavelet-based multi-scale temporal imputation. It utilizes the discrete wavelet transform to decompose a time series into multiple scales, each with a distinct time–frequency resolution and specific frequency range, enabling full-spectrum utilization. To effectively transfer multi-scale temporal dynamics from the source domain to the target domain, we introduce a multi-scale temporal imputation module which assigns a deep neural network to perform the temporal imputation task on the sequence at each scale, learning scale-specific domain-invariant information. We further design an energy-based multi-scale weighting strategy, which adaptively integrates information from multiple scales based on the frequency distribution of the input data to improve the transfer performance of the model. Extensive experiments on three real-world time series datasets demonstrate that LEMON significantly outperforms the state-of-the-art methods, achieving an average improvement of 4.45% in accuracy and 6.29% in MF1-score.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107428"},"PeriodicalIF":6.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759541","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}

引用次数: 0

Multi-level feature fusion networks for smoke recognition in remote sensing imagery. 多尺度特征融合网络用于遥感图像烟雾识别。

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-01 Epub Date: 2025-01-04 DOI: 10.1016/j.neunet.2024.107112

Yupeng Wang, Yongli Wang, Zaki Ahmad Khan, Anqi Huang, Jianghui Sang

{"title":"Multi-level feature fusion networks for smoke recognition in remote sensing imagery.","authors":"Yupeng Wang, Yongli Wang, Zaki Ahmad Khan, Anqi Huang, Jianghui Sang","doi":"10.1016/j.neunet.2024.107112","DOIUrl":"10.1016/j.neunet.2024.107112","url":null,"abstract":"<p><p>Smoke is a critical indicator of forest fires, often detectable before flames ignite. Accurate smoke identification in remote sensing images is vital for effective forest fire monitoring within Internet of Things (IoT) systems. However, existing detection methods frequently falter in complex real-world scenarios, where variable smoke shapes and sizes, intricate backgrounds, and smoke-like phenomena (e.g., clouds and haze) lead to missed detections and false alarms. To address these challenges, we propose the Multi-level Feature Fusion Network (MFFNet), a novel framework grounded in contrastive learning. MFFNet begins by extracting multi-scale features from remote sensing images using a pre-trained ConvNeXt model, capturing information across different levels of granularity to accommodate variations in smoke appearance. The Attention Feature Enhancement Module further refines these multi-scale features, enhancing fine-grained, discriminative attributes relevant to smoke detection. Subsequently, the Bilinear Feature Fusion Module combines these enriched features, effectively reducing background interference and improving the model's ability to distinguish smoke from visually similar phenomena. Finally, contrastive feature learning is employed to improve robustness against intra-class variations by focusing on unique regions within the smoke patterns. Evaluated on the benchmark dataset USTC_SmokeRS, MFFNet achieves an accuracy of 98.87%. Additionally, our model demonstrates a detection rate of 94.54% on the extended E_SmokeRS dataset, with a low false alarm rate of 3.30%. These results highlight the effectiveness of MFFNet in recognizing smoke in remote sensing images, surpassing existing methodologies. The code is accessible at https://github.com/WangYuPeng1/MFFNet.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"184 ","pages":"107112"},"PeriodicalIF":6.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967303","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}

引用次数: 0

ICH-PRNet: a cross-modal intracerebral haemorrhage prognostic prediction method using joint-attention interaction mechanism. ICH-PRNet：基于联合注意相互作用机制的跨模式脑出血预后预测方法。

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-01 Epub Date: 2025-01-06 DOI: 10.1016/j.neunet.2024.107096

Xinlei Yu, Ahmed Elazab, Ruiquan Ge, Jichao Zhu, Lingyan Zhang, Gangyong Jia, Qing Wu, Xiang Wan, Lihua Li, Changmiao Wang

{"title":"ICH-PRNet: a cross-modal intracerebral haemorrhage prognostic prediction method using joint-attention interaction mechanism.","authors":"Xinlei Yu, Ahmed Elazab, Ruiquan Ge, Jichao Zhu, Lingyan Zhang, Gangyong Jia, Qing Wu, Xiang Wan, Lihua Li, Changmiao Wang","doi":"10.1016/j.neunet.2024.107096","DOIUrl":"10.1016/j.neunet.2024.107096","url":null,"abstract":"<p><p>Accurately predicting intracerebral hemorrhage (ICH) prognosis is a critical and indispensable step in the clinical management of patients post-ICH. Recently, integrating artificial intelligence, particularly deep learning, has significantly enhanced prediction accuracy and alleviated neurosurgeons from the burden of manual prognosis assessment. However, uni-modal methods have shown suboptimal performance due to the intricate pathophysiology of the ICH. On the other hand, existing cross-modal approaches that incorporate tabular data have often failed to effectively extract complementary information and cross-modal features between modalities, thereby limiting their prognostic capabilities. This study introduces a novel cross-modal network, ICH-PRNet, designed to predict ICH prognosis outcomes. Specifically, we propose a joint-attention interaction encoder that effectively integrates computed tomography images and clinical texts within a unified representational space. Additionally, we define a multi-loss function comprising three components to comprehensively optimize cross-modal fusion capabilities. To balance the training process, we employ a self-adaptive dynamic prioritization algorithm that adjusts the weights of each component, accordingly. Our model, through these innovative designs, establishes robust semantic connections between modalities and uncovers rich, complementary cross-modal information, thereby achieving superior prediction results. Extensive experimental results and comparisons with state-of-the-art methods on both in-house and publicly available datasets unequivocally demonstrate the superiority and efficacy of the proposed method. Our code is at https://github.com/YU-deep/ICH-PRNet.git.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"184 ","pages":"107096"},"PeriodicalIF":6.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972996","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}

引用次数: 0

Neighborhood relation-based knowledge distillation for image classification

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-01 DOI: 10.1016/j.neunet.2025.107429

Jianping Gou , Xiaomeng Xin , Baosheng Yu , Heping Song , Weiyong Zhang , Shaohua Wan

{"title":"Neighborhood relation-based knowledge distillation for image classification","authors":"Jianping Gou , Xiaomeng Xin , Baosheng Yu , Heping Song , Weiyong Zhang , Shaohua Wan","doi":"10.1016/j.neunet.2025.107429","DOIUrl":"10.1016/j.neunet.2025.107429","url":null,"abstract":"<div><div>As an efficient model compression method, recent knowledge distillation methods primarily transfer the knowledge from a large teacher model to a small student model by minimizing the differences between the predictions from teacher and student. However, the relationship between different samples has not been well-investigated, since recent relational distillation methods mainly construct the knowledge from all randomly selected samples, e.g., the similarity matrix of mini-batch samples. In this paper, we propose <strong>N</strong>eighborhood <strong>R</strong>elation-Based <strong>K</strong>nowledge <strong>D</strong>istillation (NRKD) to consider the local structure as the novel relational knowledge for better knowledge transfer. Specifically, we first find a subset of samples with their <span><math><mi>K</mi></math></span>-nearest neighbors according to the similarity matrix of mini-batch samples and then build the neighborhood relationship knowledge for knowledge distillation, where the characterized relational knowledge can be transferred by both intermediate feature maps and output logits. We perform extensive experiments on several popular image classification datasets for knowledge distillation, including CIFAR10, CIFAR100, Tiny ImageNet, and ImageNet. Experimental results demonstrate that the proposed NRKD yields competitive results, compared to the state-of-the art distillation methods. Our codes are available at: <span><span>https://github.com/xinxiaoxiaomeng/NRKD.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107429"},"PeriodicalIF":6.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759539","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}

引用次数: 0

Identity Model Transformation for boosting performance and efficiency in object detection network. 身份模型转换提高目标检测网络的性能和效率。

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-01 Epub Date: 2024-12-31 DOI: 10.1016/j.neunet.2024.107098

Zhongyuan Lu, Jin Liu, Miaozhong Xu

{"title":"Identity Model Transformation for boosting performance and efficiency in object detection network.","authors":"Zhongyuan Lu, Jin Liu, Miaozhong Xu","doi":"10.1016/j.neunet.2024.107098","DOIUrl":"10.1016/j.neunet.2024.107098","url":null,"abstract":"<p><p>Modifying the structure of an existing network is a common method to further improve the performance of the network. However, modifying some layers in network often results in pre-trained weight mismatch, and fine-tune process is time-consuming and resource-inefficient. To address this issue, we propose a novel technique called Identity Model Transformation (IMT), which keep the output before and after transformation in an equal form by rigorous algebraic transformations. This approach ensures the preservation of the original model's performance when modifying layers. Additionally, IMT significantly reduces the total training time required to achieve optimal results while further enhancing network performance. IMT has established a bridge for rapid transformation between model architectures, enabling a model to quickly perform analytic continuation and derive a family of tree-like models with better performance. This model family possesses a greater potential for optimization improvements compared to a single model. Extensive experiments across various object detection tasks validated the effectiveness and efficiency of our proposed IMT solution, which saved 94.76% time in fine-tuning the basic model YOLOv4-Rot on DOTA 1.5 dataset, and by using the IMT method, we saw stable performance improvements of 9.89%, 6.94%, 2.36%, and 4.86% on the four datasets: AI-TOD, DOTA1.5, coco2017, and MRSAText, respectively.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"184 ","pages":"107098"},"PeriodicalIF":6.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957832","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}

引用次数: 0

Synergistic learning with multi-task DeepONet for efficient PDE problem solving. 协同学习与多任务DeepONet的高效PDE问题求解。

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-01 Epub Date: 2025-01-03 DOI: 10.1016/j.neunet.2024.107113

Varun Kumar, Somdatta Goswami, Katiana Kontolati, Michael D Shields, George Em Karniadakis

{"title":"Synergistic learning with multi-task DeepONet for efficient PDE problem solving.","authors":"Varun Kumar, Somdatta Goswami, Katiana Kontolati, Michael D Shields, George Em Karniadakis","doi":"10.1016/j.neunet.2024.107113","DOIUrl":"10.1016/j.neunet.2024.107113","url":null,"abstract":"<p><p>Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine learning to address issues such as data sparsity and overfitting in neural networks. In this work, we apply MTL to problems in science and engineering governed by partial differential equations (PDEs). However, implementing MTL in this context is complex, as it requires task-specific modifications to accommodate various scenarios representing different physical processes. To this end, we present a multi-task deep operator network (MT-DeepONet) to learn solutions across various functional forms of source terms in a PDE and multiple geometries in a single concurrent training session. We introduce modifications in the branch network of the vanilla DeepONet to account for various functional forms of a parameterized coefficient in a PDE. Additionally, we handle parameterized geometries by introducing a binary mask in the branch network and incorporating it into the loss term to improve convergence and generalization to new geometry tasks. Our approach is demonstrated on three benchmark problems: (1) learning different functional forms of the source term in the Fisher equation; (2) learning multiple geometries in a 2D Darcy Flow problem and showcasing better transfer learning capabilities to new geometries; and (3) learning 3D parameterized geometries for a heat transfer problem and demonstrate the ability to predict on new but similar geometries. Our MT-DeepONet framework offers a novel approach to solving PDE problems in engineering and science under a unified umbrella based on synergistic learning that reduces the overall training cost for neural operators.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"184 ","pages":"107113"},"PeriodicalIF":6.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967318","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}

引用次数: 0

Enhancing Recommender Systems through Imputation and Social-Aware Graph Convolutional Neural Network. 基于归算和社会感知图卷积神经网络的推荐系统增强。

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-04-01 Epub Date: 2024-12-31 DOI: 10.1016/j.neunet.2024.107071

Azadeh Faroughi, Parham Moradi, Mahdi Jalili

{"title":"Enhancing Recommender Systems through Imputation and Social-Aware Graph Convolutional Neural Network.","authors":"Azadeh Faroughi, Parham Moradi, Mahdi Jalili","doi":"10.1016/j.neunet.2024.107071","DOIUrl":"10.1016/j.neunet.2024.107071","url":null,"abstract":"<p><p>Recommendation systems are vital tools for helping users discover content that suits their interests. Collaborative filtering methods are one of the techniques employed for analyzing interactions between users and items, which are typically stored in a sparse matrix. This inherent sparsity poses a challenge because it necessitates accurately and effectively filling in these gaps to provide users with meaningful and personalized recommendations. Our solution addresses sparsity in recommendations by incorporating diverse data sources, including trust statements and an imputation graph. The trust graph captures user relationships and trust levels, working in conjunction with an imputation graph, which is constructed by estimating the missing rates of each user based on the user-item matrix using the average rates of the most similar users. Combined with the user-item rating graph, an attention mechanism fine tunes the influence of these graphs, resulting in more personalized and effective recommendations. Our method consistently outperforms state-of-the-art recommenders in real-world dataset evaluations, underscoring its potential to strengthen recommendation systems and mitigate sparsity challenges.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"184 ","pages":"107071"},"PeriodicalIF":6.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967247","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}

引用次数: 0

Deep prior embedding method for Electrical Impedance Tomography

IF 6 1区计算机科学

Neural Networks Pub Date : 2025-03-31 DOI: 10.1016/j.neunet.2025.107419

Junwu Wang , Jiansong Deng , Dong Liu

{"title":"Deep prior embedding method for Electrical Impedance Tomography","authors":"Junwu Wang , Jiansong Deng , Dong Liu","doi":"10.1016/j.neunet.2025.107419","DOIUrl":"10.1016/j.neunet.2025.107419","url":null,"abstract":"<div><div>This paper presents a novel deep learning-based approach for Electrical Impedance Tomography (EIT) reconstruction that effectively integrates image priors to enhance reconstruction quality. Traditional neural network methods often rely on random initialization, which may not fully exploit available prior information. Our method addresses this by using image priors to guide the initialization of the neural network, allowing for a more informed starting point and better utilization of prior knowledge throughout the reconstruction process. We explore three different strategies for embedding prior information: non-prior embedding, implicit prior embedding, and full prior embedding. Through simulations and experimental studies, we demonstrate that the incorporation of accurate image priors significantly improves the fidelity of the reconstructed conductivity distribution. The method is robust across varying levels of noise in the measurement data, and the quality of the reconstruction is notably higher when the prior closely resembles the true distribution. This work highlights the importance of leveraging prior information in EIT and provides a framework that could be extended to other inverse problems where prior knowledge is available.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"188 ","pages":"Article 107419"},"PeriodicalIF":6.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759542","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}

引用次数: 0