IEEE transactions on neural networks and learning systems最新文献_第7页

Diffusion Models, Image Super-Resolution, and Everything: A Survey 扩散模型、图像超分辨率与一切：概览

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-10-29 DOI: 10.1109/tnnls.2024.3476671

Brian B. Moser, Arundhati S. Shanbhag, Federico Raue, Stanislav Frolov, Sebastian Palacio, Andreas Dengel

引用次数: 0

Representation Learning Based on Co-Evolutionary Combined With Probability Distribution Optimization for Precise Defect Location. 基于协同进化结合概率分布优化的表征学习，用于精确缺陷定位。

IF 10.2 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-10-28 DOI: 10.1109/TNNLS.2024.3479734

Jinglin Zhang, Zekai Zhang, Qinghui Chen, Gang Li, Weiyu Li, Shijiao Ding, Maomao Xiong, Wenhao Zhang, Shengyong Chen

{"title":"Representation Learning Based on Co-Evolutionary Combined With Probability Distribution Optimization for Precise Defect Location.","authors":"Jinglin Zhang, Zekai Zhang, Qinghui Chen, Gang Li, Weiyu Li, Shijiao Ding, Maomao Xiong, Wenhao Zhang, Shengyong Chen","doi":"10.1109/TNNLS.2024.3479734","DOIUrl":"https://doi.org/10.1109/TNNLS.2024.3479734","url":null,"abstract":"Visual defect detection methods based on representation learning play an important role in industrial scenarios. Defect detection technology based on representation learning has made significant progress. However, existing defect detection methods still face three challenges: first, the extreme scarcity of industrial defect samples makes training difficult. Second, due to the characteristics of industrial defects, such as blur and background interference, it is challenging to obtain fuzzy defect separation edges and context information. Third, industrial defects cannot obtain accurate positioning information. This article proposes feature co-evolution interaction architecture (CIA) and glass container defect dataset to address the above challenges. Specifically, the contributions of this article are as follows: first, this article designs a glass container image acquisition system that combines RGB and polarization information to create a glass container defect dataset containing more than 60 000 samples to alleviate the sample scarcity problem in industrial scenarios. Subsequently, this article designs the CIA. CIA optimizes the probability distribution of features through the co-evolution of edge and context features, thereby improving detection accuracy in blurred defects and noisy environments. Finally, this article proposes a novel inforced IoU loss (IIoU loss), which can obtain more accurate position information by being aware of the scale changes of the predicted box. Defect detection experiments in three mainstream industrial manufacturing categories (Northeastern University (NEU)-Det, glass containers, wood) show that CIA only uses 22.5 GFLOPs, and mean average precision (mAP) (NEU-Det: 88.74%, glass containers: 95.38%, wood: 68.42%) outperforms state-of-the-art methods.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"PP ","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521808","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

Scale-Aware Super-Resolution Network With Dual Affinity Learning for Lesion Segmentation From Medical Images. 利用双重亲和力学习的规模感知超分辨率网络用于医学图像的病变分割

IF 10.2 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-10-28 DOI: 10.1109/TNNLS.2024.3477947

Luyang Luo, Yanwen Li, Zhizhong Chai, Huangjing Lin, Pheng-Ann Heng, Hao Chen

{"title":"Scale-Aware Super-Resolution Network With Dual Affinity Learning for Lesion Segmentation From Medical Images.","authors":"Luyang Luo, Yanwen Li, Zhizhong Chai, Huangjing Lin, Pheng-Ann Heng, Hao Chen","doi":"10.1109/TNNLS.2024.3477947","DOIUrl":"https://doi.org/10.1109/TNNLS.2024.3477947","url":null,"abstract":"Convolutional neural networks (CNNs) have shown remarkable progress in medical image segmentation. However, the lesion segmentation remains a challenge to state-of-the-art CNN-based algorithms due to the variance in scales and shapes. On the one hand, tiny lesions are hard to delineate precisely from the medical images which are often of low resolutions. On the other hand, segmenting large-size lesions requires large receptive fields, which exacerbates the first challenge. In this article, we present a scale-aware super-resolution (SR) network to adaptively segment lesions of various sizes from low-resolution (LR) medical images. Our proposed network contains dual branches to simultaneously conduct lesion mask SR (LMSR) and lesion image SR (LISR). Meanwhile, we introduce scale-aware dilated convolution (SDC) blocks into the multitask decoders to adaptively adjust the receptive fields of the convolutional kernels according to the lesion sizes. To guide the segmentation branch to learn from richer high-resolution (HR) features, we propose a feature affinity (FA) module and a scale affinity (SA) module to enhance the multitask learning of the dual branches. On multiple challenging lesion segmentation datasets, our proposed network achieved consistent improvements compared with other state-of-the-art methods. Code will be available at: https://github.com/poiuohke/SASR_Net.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"PP ","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521809","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

Weighted Hermite Variable Projection Networks for Classifying Visually Evoked Potentials. 用于视觉诱发电位分类的加权赫尔墨特变量投影网络

IF 10.2 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-10-28 DOI: 10.1109/TNNLS.2024.3475271

Tamas Dozsa, Carl Bock, Jens Meier, Peter Kovacs

{"title":"Weighted Hermite Variable Projection Networks for Classifying Visually Evoked Potentials.","authors":"Tamas Dozsa, Carl Bock, Jens Meier, Peter Kovacs","doi":"10.1109/TNNLS.2024.3475271","DOIUrl":"https://doi.org/10.1109/TNNLS.2024.3475271","url":null,"abstract":"The occipital cortex responds to visual stimuli regardless of a patient's level of consciousness or attention, offering a noninvasive diagnostic tool for both ophthalmologists and neurologists. This response signal manifests as a unique waveform referred to as the visually evoked potential (VEP), which can be extracted from the electroencephalogram (EEG) activity of a human being. We propose a trainable VEP representation to disentangle the underlying explanatory factors of the data. To enhance the learning process with domain knowledge, we present an innovative parameterization of classical Hermite functions that effectively captures VEP pattern variations arising from patient-specific factors, disorders, and measurement setup influences. Then, we introduce a differentiable variable projection (VP) layer to fuse Hermite basis function expansions (BFEs) of VEP signals with machine learning (ML) approaches. We prove the existence of an optimal set of parameters in the least-squares sense, assess the representation power of such layers, and calculate their analytical derivatives, which allows us to utilize backpropagation for training. Finally, we evaluate the effectiveness of the proposed learning framework in VEP-based color classification. To achieve this, we have designed a novel measurement system dedicated to intraoperative clinical use cases, which presents new ways for patient monitoring during neurosurgical procedures.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"PP ","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521810","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

DSE-Based Hardware Trojan Attack for Neural Network Accelerators on FPGAs. 基于 DSE 的 FPGA 神经网络加速器硬件木马攻击。

IF 10.2 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-10-28 DOI: 10.1109/TNNLS.2024.3482364

Chao Guo, Masao Yanagisawa, Youhua Shi

{"title":"DSE-Based Hardware Trojan Attack for Neural Network Accelerators on FPGAs.","authors":"Chao Guo, Masao Yanagisawa, Youhua Shi","doi":"10.1109/TNNLS.2024.3482364","DOIUrl":"https://doi.org/10.1109/TNNLS.2024.3482364","url":null,"abstract":"Over the past few years, the emergence and development of design space exploration (DSE) have shortened the deployment cycle of deep neural networks (DNNs). As a result, with these open-sourced DSE, we can automatically compute the optimal configuration and generate the corresponding accelerator intellectual properties (IPs) from the pretrained neural network models and hardware constraints. However, to date, the security of DSE has received little attention. Therefore, we explore this issue from an adversarial perspective and propose an automated hardware Trojan (HT) generation framework embedded within DSE. The framework uses an evolutionary algorithm (EA) to analyze user-input data to automatically generate the attack code before placing it in the final output accelerator IPs. The proposed HT is sufficiently stealthy and suitable for both single and multifield-programmable gate array (FPGA) designs. It can also implement controlled accuracy degradation attacks and specified category attacks. We conducted experiments on LeNet, VGG-16, and YOLO, respectively, and found that for the LeNet model trained on the CIFAR-10 dataset, attacking only one kernel resulted in 97.3% of images being classified in the category specified by the adversary and reduced accuracy by 59.58%. Moreover, for the VGG-16 model trained on the ImageNet dataset, attacking eight kernels can cause up to 96.53% of the images to be classified into the category specified by the adversary and causes the model's accuracy to decrease to 2.5%. Finally, for the YOLO model trained on the PASCAL VOC dataset, attacking with eight kernels can cause the model to identify the target as the specified category and cause slight perturbations to the bounding boxes. Compared to the un-compromised designs, the look-up tables (LUTs) overhead of the proposed HT design does not exceed 0.6%.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"PP ","pages":""},"PeriodicalIF":10.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521807","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

Exploring Multiconnectivity and Subdivision Functions of Brain Network via Heterogeneous Graph Network for Cognitive Disorder Identification 通过异构图网络探索脑网络的多连接性和细分功能，用于认知障碍识别

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-10-25 DOI: 10.1109/tnnls.2024.3481667

Dongdong Chen, Mengjun Liu, Zhenrong Shen, Linlin Yao, Xiangyu Zhao, Zhiyun Song, Haolei Yuan, Qian Wang, Lichi Zhang

引用次数: 0

Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics Federated Unlearning：关于方法、设计指南和评估指标的调查

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-10-25 DOI: 10.1109/tnnls.2024.3478334

Nicolò Romandini, Alessio Mora, Carlo Mazzocca, Rebecca Montanari, Paolo Bellavista

引用次数: 0

Token-Bucket-Protocol-Based Recursive Remote State Estimation for Complex Networks Under Amplify-and-Forward Relays 基于令牌桶协议的递归远程状态估计，适用于放大和前向中继下的复杂网络

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-10-25 DOI: 10.1109/tnnls.2024.3474016

Tong-Jian Liu, Zidong Wang, Yang Liu, Rui Wang

引用次数: 0

Sociological-Theory-Based Multitopic Self-Supervised Recommendation 基于社会学理论的多主题自我监督推荐

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-10-24 DOI: 10.1109/tnnls.2024.3477720

Qin Zhao, Peihan Wu, Gang Liu, Dongdong An, Jie Lian, MengChu Zhou

引用次数: 0

Point-MPP: Point Cloud Self-Supervised Learning From Masked Position Prediction Point-MPP：从屏蔽位置预测中进行点云自我监督学习

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2024-10-24 DOI: 10.1109/tnnls.2024.3479309

Songlin Fan, Wei Gao, Ge Li

引用次数: 0