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3rd Asia Conference on Algorithms, Computing and Machine Learning, CACML 2024

作者： Xu, Can Huang, Yaqin Xiang, Xinni Lei, Haike Yang, Jie Shanghai Jiao Tong University Shanghai China Chongqing University Cancer Hospital Chongqing China West China Hospital Sichuan University Chengdu China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition China Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment China West China School of Medicine China Chongqing Cancer Multi-omics Big Data Application Engineering Research Center China

ISBN: (纸本)9798400716416

Machine Learning (ML) has been widely applied to medical science for decades. As common knowledge, the progress of many diseases is often chronic and dynamic. Longitudinal data, or time-series data, has better descriptions of certain diseases, as it naturally captures the dynamic over time. Venous thromboembolism (VTE), as a regular complication of cancer tumour treatments, has been emphasized in many studies. Recent research has shown that ML algorithms incredibly contribute to VTE risk estimation and prediction. However, existing methods rarely consider the dynamic changes during the development of VTE, and often make decisions based on time-point data, such as the latest blood test results of patients. In this paper, we propose a VTE detection model that takes advantage of capturing dynamic information using time-series data. The model consists of a Recurrent Neural Network (RNN) based framework with time embedding and Attention mechanism to encode longitudinal features of patients. We test our proposed model on a real-world dataset that contains multiple times of blood test results of patients together with other clinical features. The network is compared with several commonly used ML models in previous VTE studies. The results show that our model outperforms all baseline models, and achieved a state-of-art performance with 80.0% accuracy and 0.881 AUC. © 2024 ACM.

关键词： Risk assessment

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Novel Regularization Method for Reduced Biquaternion Neural Network

SSRN

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SSRN 2023年

作者： Gai, Shan Huang, Xiang School of Information Science and Engineering Yanshan University Hebei Qinhuangdao066004 China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Jiangxi Nanchang330063 China

A reduced biquaternion neural network (RQNN) is a new type of neural network framework that has achieved significant success in machine learning. However, as the reduced biquaternion algebra system contains infinite zero divisors, the RQNN is easily trapped in a local minimum and overfitted. We propose a new regularization scheme for the RQNN to address these issues. First, we propose a new operation in the reduced biquaternion domain—the reduced biquaternion complex modulus (RQCM). Second, we analyse the algebraic properties and obtain the geometric meaning of the RQCM. Finally, we design an improved weight decay method using the RQCM. Unlike conventional weight decay methods with matrix norm constraints, the proposed method can better project the reduced biquaternion in terms of the scale and phase. Colour image classification and denoising tasks are used to demonstrate the superior performance of the RQCM over state-of-the-art weight decay methods. The source code can be found at https://***/tasteimage/Reg4RQNN . © 2023, The Authors. All rights reserved.

关键词： Complex networks

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Journal of Network Intelligence

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Journal of Network Intelligence 2022年第4期7卷 848-861页

作者： Weng, Wei Li, Tong Liao, Jian-Chao Guo, Feng Chen, Fen Wei, Bo-Wen College of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Newland Auto-ID Tech. Co. Ltd Fuzhou350015 China Xiamen Fuyun Information Tech. Co. Ltd Xiamen361008 China

Graph clustering is a fundamental method for studying complex networks. Some existing approaches focus on the graph data without attributed information. However, graph data in the real world generally have attribute information, which can better describe complex networks. Recently, deep learning-based methods are widely used for attributed graph clustering. DAEGC is a goal-directed method, which utilizes graph attention encoders for latent information learning and performs competitively in attributed graph clustering. To better aggregate the structure and node attribute information in DAEGC, we employ a lightweight attention mechanism to capture the relationship between nodes and their neighbors, learn hidden representations, and use a self-training module to train the model for optimal results. Extensive experiments show that our proposed method improves DAEGC and achieves superior performance compared with state-of-the-art algorithms in attributed graph clustering. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Complex networks

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NLFA: A Non Local Fusion Alignment Module for Multi-Scale Feature in Object Detection 3rd

NLFA: A Non Local Fusion Alignment Module for Multi-Scale Fe...

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3rd International Symposium on Automation, Mechanical and Design Engineering, SAMDE 2022

作者： Xue, Honghui Ma, Jinshan Cai, Zheyi Fu, Junfang Guo, Feng Weng, Wei Dong, Yunxin Zhang, Zhenchang College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou China Fujian Zhongke Zhongxin Intelligent Technology Co. Ltd Fuzhou China Fujian Newland Auto-ID Tech. Co. Ltd Fuzhou China Department of Computer and Information Engineering Xiamen University of Technology Xiamen China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen China

ISBN: (纸本)9783031400698

Recently, in order to pursue better detection results, more convolutional layers and deeper networks are a direction pursued by everyone. However, more and more down-sampling convolution or up-sampling operations generate feature maps of different scales, which makes it difficult to avoid the loss of detailed information of the image, and the distribution of different scales features will be misaligned. In particular, the loss and dislocation of the target boundary information will affect the features learned by the model and reduce the accuracy. This paper proposes a feature alignment method based on non-local idea, and designed two modules—Non Local Align Module (NLA) and Channel Fusion Augment Module (CFA). At the same time, the neighborhood calculation algorithm is also designed for it, which strengthens the binding force on the calculation of boundary information. These two modules can be easily embedded into the current mainstream object detection network to improve the detection effect of the model. Compared to the previous model, the network using our NLA module and CFA module achieves better results than the original model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Hybrid Data-Free Knowledge Distillation 39

Hybrid Data-Free Knowledge Distillation

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Tang, Jialiang Chen, Shuo Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Center for Advanced Intelligence Project RIKEN Japan Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)157735897X

Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic examples, respectively. However, they inevitably become weak in practical scenarios due to the difficulties in gathering or emulating sufficient real-world data. To solve this problem, we propose a novel method called Hybrid Data-Free Distillation (HiDFD), which leverages only a small amount of collected data as well as generates sufficient examples for training student networks. Our HiDFD comprises two primary modules, i.e., the teacher-guided generation and student distillation. The teacher-guided generation module guides a Generative Adversarial Network (GAN) by the teacher network to produce high-quality synthetic examples from very few real-world collected examples. Specifically, we design a feature integration mechanism to prevent the GAN from overfitting and facilitate the reliable representation learning from the teacher network. Meanwhile, we drive a category frequency smoothing technique via the teacher network to balance the generative training of each category. In the student distillation module, we explore a data inflation strategy to properly utilize a blend of real and synthetic data to train the student network via a classifier-sharing-based feature alignment technique. Intensive experiments across multiple benchmarks demonstrate that our HiDFD can achieve state-of-the-art performance using 120 times less collected data than existing methods. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： Personnel training

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Learn What You Need in Personalized Federated Learning

arXiv

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arXiv 2024年

作者： Lv, Kexin Ye, Rui Huang, Xiaolin Yang, Jie Chen, Siheng Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University Shanghai200240 China Shanghai AI Laboratory Shanghai200232 China

Personalized federated learning aims to address data heterogeneity across local clients in federated learning. However, current methods blindly incorporate either full model parameters or predefined partial parameters in personalized federated learning. They fail to customize the collaboration manner according to each local client's data characteristics, causing unpleasant aggregation results. To address this essential issue, we propose Learn2pFed, a novel algorithm-unrolling-based personalized federated learning framework, enabling each client to adaptively select which part of its local model parameters should participate in collaborative training. The key novelty of the proposed Learn2pFed is to optimize each local model parameter's degree of participant in collaboration as learnable parameters via algorithm unrolling methods. This approach brings two benefits: 1) mathmatically determining the participation degree of local model parameters in the federated collaboration, and 2) obtaining more stable and improved solutions. Extensive experiments on various tasks, including regression, forecasting, and image classification, demonstrate that Learn2pFed significantly outperforms previous personalized federated learning methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Parameter estimation

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A Research Mode Based Evolutionary Algorithm for Many-Objective Optimization

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Chinese Journal of Electronics 2019年第4期28卷 764-772页

作者： CHEN Guoyu LI Junhua Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University

The development of algorithms to solve Many-objective optimization problems(MaOPs) has attracted significant research interest in recent *** various types of Pareto front(PF) is a daunting challenge for evolutionary algorithm. A Research mode based evolutionary algorithm(RMEA) is proposed for many-objective optimization. The archive in the RMEA is used to store non-dominated solutions that can reflect the shape of the PF to guide the reference vector *** concerning the population is collected, once the number of non-dominated solutions reaches its limit after many generations without exceeding a given threshold, RMEA introduces a research mode that generates more reference vectors to search through the solutions. The proposed algorithm showed competitive performance with four state-of-the-art evolutionary algorithms in a large number of experiments.

关键词： Many-objective optimization Reference vector Research mode Evolutionary algorithm

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Generating Cartoon images from Face Photos with Cycle-Consistent Adversarial Networks

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Computers, Materials & Continua 2021年第11期69卷 2733-2747页

作者： Tao Zhang Zhanjie Zhang Wenjing Jia Xiangjian He Jie Yang School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214000China Key Laboratory of Artificial Intelligence Jiangsu214000China The Global Big Data Technologies Centre University of Technology SydneyUltimoNSW2007Australia The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong UniversityShanghai201100China

The generative adversarial network(GAN)is first proposed in 2014,and this kind of network model is machine learning systems that can learn to measure a given distribution of data,one of the most important applications is style *** transfer is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output ***-GAN is a classic GAN model,which has a wide range of scenarios in style *** its unsupervised learning characteristics,the mapping is easy to be learned between an input image and an output ***,it is difficult for CYCLE-GAN to converge and generate high-quality *** order to solve this problem,spectral normalization is introduced into each convolutional kernel of the *** convolutional kernel reaches Lipschitz stability constraint with adding spectral normalization and the value of the convolutional kernel is limited to[0,1],which promotes the training process of the proposed ***,we use pretrained model(VGG16)to control the loss of image content in the position of l1 *** avoid overfitting,l1 regularization term and l2 regularization term are both used in the object loss *** terms of Frechet Inception Distance(FID)score evaluation,our proposed model achieves outstanding performance and preserves more discriminative *** results show that the proposed model converges faster and achieves better FID scores than the state of the art.

关键词： Generative adversarial network spectral normalization Lipschitz stability constraint VGG16 l1 regularization term l2 regularization term Frechet inception distance

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Online LiDAR-Camera Extrinsic Parameters Self-checking

arXiv

引用

arXiv 2022年

作者： Wei, Pengjin Yan, Guohang Li, Yikang Fang, Kun Yang, Jie Liu, Wei The Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China The Autonomous Driving Group Shanghai AI Laboratory China

With the development of neural networks and the increasing popularity of automatic driving, the calibration of the LiDAR and the camera has attracted more and more attention. This calibration task is multi-modal, where the rich color and texture information captured by the camera and the accurate three-dimensional spatial information from the LiDAR is incredibly significant for downstream tasks. Current research interests mainly focus on obtaining accurate calibration results through information fusion. However, they seldom analyze whether the calibrated results are correct or not, which could be of significant importance in real-world applications. For example, in large-scale production, the LiDARs and the cameras of each smart car have to get well-calibrated as the car leaves the production line, while in the rest of the car life period, the poses of the LiDARs and cameras should also get continually supervised to ensure the security. To this end, this paper proposes a self-checking algorithm to judge whether the extrinsic parameters are well-calibrated by introducing a binary classification network based on the fused information from the camera and the LiDAR. Moreover, since there is no such dataset for the task in this work, we further generate a new dataset branch from the KITTI dataset tailored for the task. Our experiments on the proposed dataset branch demonstrate the performance of our method. To the best of our knowledge, this is the first work to address the significance of continually checking the calibrated extrinsic parameters for autonomous driving. The code is open-sourced on the Github website at https://***/OpenCalib/LiDAR2camera_self-check. Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

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Semantic Transformation-Based Data Augmentation for Few-Shot Learning

SSRN

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SSRN 2023年

作者： Pan, Mei-Hong Xin, Hong-Yi Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiaotong University Shanghai200240 China

Few-shot learning (FSL) as a data-scarce method, aims to recognize instances of unseen classes solely based on very few examples. However, the model can easily become overfitted due to the biased distribution formed with extremely limited training data. This paper presents a task specific data augmentation approach by transferring samples from base dataset to the novel tasks in an encoder-decoder paradigm, which guarantees generating semantically meaningful features. Specifically, the feature transfer process is carried out in semantic space. We further impose a compactness constraint to the generated features with the prototypes working as the reference points, which ensures the generated features distribute around the class centers. Moreover, we incorporate the cluster centers of the query set with the prototypes of the support set to reduce the bias of the class centers. With the supervision of the compactness loss, the model is encouraged to generate discriminative features with high inter-class dispersion and intra-class compactness. Extensive experiments show that our method outperforms the state-of-the-arts on four benchmarks, namely MiniimageNet, TieredimageNet, CUB and CIFAR-FS. © 2023, The Authors. All rights reserved.

关键词： Motion planning

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