Tsinghua Science and Technology最新文献

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Ensemble Knowledge Distillation for Federated Semi-Supervised Image Classification 联合半监督图像分类的集合知识提炼
IF 6.6 1区 计算机科学
Tsinghua Science and Technology Pub Date : 2024-09-11 DOI: 10.26599/TST.2023.9010156
Ertong Shang;Hui Liu;Jingyang Zhang;Runqi Zhao;Junzhao Du
{"title":"Ensemble Knowledge Distillation for Federated Semi-Supervised Image Classification","authors":"Ertong Shang;Hui Liu;Jingyang Zhang;Runqi Zhao;Junzhao Du","doi":"10.26599/TST.2023.9010156","DOIUrl":"https://doi.org/10.26599/TST.2023.9010156","url":null,"abstract":"Federated learning is an emerging privacy-preserving distributed learning paradigm, in which many clients collaboratively train a shared global model under the orchestration of a remote server. Most current works on federated learning have focused on fully supervised learning settings, assuming that all the data are annotated with ground-truth labels. However, this work considers a more realistic and challenging setting, Federated Semi-Supervised Learning (FSSL), where clients have a large amount of unlabeled data and only the server hosts a small number of labeled samples. How to reasonably utilize the server-side labeled data and the client-side unlabeled data is the core challenge in this setting. In this paper, we propose a new FSSL algorithm for image classification based on consistency regularization and ensemble knowledge distillation, called EKDFSSL. Our algorithm uses the global model as the teacher in consistency regularization methods to enhance both the accuracy and stability of client-side unsupervised learning on unlabeled data. Besides, we introduce an additional ensemble knowledge distillation loss to mitigate model overfitting during server-side retraining on labeled data. Extensive experiments on several image classification datasets show that our EKDFSSL outperforms current baseline methods.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"112-123"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676340","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Exploration and Practice of Constructing Trusted Public IT Systems Using Blockchain-Based Service Network 利用区块链服务网络构建可信公共 IT 系统的探索与实践
IF 6.6 1区 计算机科学
Tsinghua Science and Technology Pub Date : 2024-09-11 DOI: 10.26599/TST.2023.9010159
Zhiguang Shan;Xu Chen;Yanqiang Zhang;Yifan He;Dandan Wang
{"title":"Exploration and Practice of Constructing Trusted Public IT Systems Using Blockchain-Based Service Network","authors":"Zhiguang Shan;Xu Chen;Yanqiang Zhang;Yifan He;Dandan Wang","doi":"10.26599/TST.2023.9010159","DOIUrl":"https://doi.org/10.26599/TST.2023.9010159","url":null,"abstract":"Blockchain is one of the most influential technologies in the new round of digital economy development. In order to promote the prosperity of the digital economy with blockchain technology, we need to understand the essence of blockchain and the actual demands of relevant business. This paper delves into the nature of blockchain as a broadcast transmission technology from the perspective of technology evolution and analyzes the necessity of building a blockchain-based public Information Technology (IT) system. In addition, this paper analyzes the architecture, characteristics, and applications regarding trusted public IT system construction by drawing on the design ideas and architecture of Blockchain-based Service Network (BSN).","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"124-134"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676342","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Exploring the Chameleon Effect of Contextual Dynamics in Temporal Knowledge Graph for Event Prediction 探索用于事件预测的时态知识图谱中上下文动态的变色龙效应
IF 6.6 1区 计算机科学
Tsinghua Science and Technology Pub Date : 2024-09-11 DOI: 10.26599/TST.2024.9010067
Xin Liu;Yi He;Wenxin Tai;Xovee Xu;Fan Zhou;Guangchun Luo
{"title":"Exploring the Chameleon Effect of Contextual Dynamics in Temporal Knowledge Graph for Event Prediction","authors":"Xin Liu;Yi He;Wenxin Tai;Xovee Xu;Fan Zhou;Guangchun Luo","doi":"10.26599/TST.2024.9010067","DOIUrl":"https://doi.org/10.26599/TST.2024.9010067","url":null,"abstract":"The ability to forecast future events brings great benefits for society and cyberspace in many public safety domains, such as civil unrest, pandemics and crimes. The occurrences of new events are often correlated or dependent on historical and concurrent events. Many existing studies learn event-occurring processes with sequential and structural models, which, however, suffer from inefficient and inaccurate prediction problems. To better understand the event forecasting task and characterize the occurrence of new events, we exploit the human cognitive theory from the cognitive neuroscience discipline to find available cues for algorithm design and event prediction. Motivated by the dual process theory, we propose a two-stage learning scheme for event knowledge mining and prediction. First, we screen out event candidates based on historical inherent knowledge. Then we re-rank event candidates by probing into the newest relative events. Our proposed model mimics a sociological phenomenon called “the chameleon effect” and consists of a new target attentive graph collaborative learning mechanism to ensure a better understanding of sophisticated evolution patterns associated with events. In addition, self-supervised contrastive learning is employed to alleviate the over-smoothing problem that existed in graph learning while improving the model's interpretability. Experiments show the effectiveness of our approach.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"433-455"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676361","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Jamming-Resilient Consensus for Wireless Blockchain Networks 无线区块链网络的抗干扰共识
IF 6.6 1区 计算机科学
Tsinghua Science and Technology Pub Date : 2024-09-11 DOI: 10.26599/TST.2023.9010160
Yifei Zou;Meng Hou;Li Yang;Minghui Xu;Libing Wu;Dongxiao Yu;Xiuzhen Cheng
{"title":"Jamming-Resilient Consensus for Wireless Blockchain Networks","authors":"Yifei Zou;Meng Hou;Li Yang;Minghui Xu;Libing Wu;Dongxiao Yu;Xiuzhen Cheng","doi":"10.26599/TST.2023.9010160","DOIUrl":"https://doi.org/10.26599/TST.2023.9010160","url":null,"abstract":"As the device complexity keeps increasing, the blockchain networks have been celebrated as the cornerstone of numerous prominent platforms owing to their ability to provide distributed and immutable ledgers and data-driven autonomous organizations. The distributed consensus algorithm is the core component that directly dictates the performance and properties of blockchain networks. However, the inherent characteristics of the shared wireless medium, such as fading, interference, and openness, pose significant challenges to achieving consensus within these networks, especially in the presence of malicious jamming attacks. To cope with the severe consensus problem, in this paper, we present a distributed jamming-resilient consensus algorithm for blockchain networks in wireless environments, where the adversary can jam the communication channel by injecting jamming signals. Based on a non-binary slight jamming model, we propose a distributed four-stage algorithm to achieve consensus in the wireless blockchain network, including leader election, leader broadcast, leader aggregation, and leader announcement stages. With high probability, we prove that our jamming-resilient algorithm can ensure the validity, agreement, termination, and total order properties of consensus with the time complexity of \u0000<tex>$O(n)$</tex>\u0000. Both theoretical analyses and empirical simulations are conducted to verify the consistency and efficiency of our algorithm.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"262-278"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676355","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Global Spatial-Temporal Information Encoder-Decoder Based Action Segmentation in Untrimmed Video 基于全局时空信息编码器-解码器的无剪辑视频中的动作分割
IF 6.6 1区 计算机科学
Tsinghua Science and Technology Pub Date : 2024-09-11 DOI: 10.26599/TST.2024.9010041
Yichao Liu;Yiyang Sun;Zhide Chen;Chen Feng;Kexin Zhu
{"title":"Global Spatial-Temporal Information Encoder-Decoder Based Action Segmentation in Untrimmed Video","authors":"Yichao Liu;Yiyang Sun;Zhide Chen;Chen Feng;Kexin Zhu","doi":"10.26599/TST.2024.9010041","DOIUrl":"https://doi.org/10.26599/TST.2024.9010041","url":null,"abstract":"Action segmentation has made significant progress, but segmenting and recognizing actions from untrimmed long videos remains a challenging problem. Most state-of-the-art methods focus on designing models based on temporal convolution. However, the limitations of modeling long-term temporal dependencies and the inflexibility of temporal convolutions restrict the potential of these models. To address the issue of over-segmentation in existing action segmentation methods, which leads to classification errors and reduced segmentation quality, this paper proposes a global spatial-temporal information encoder-decoder based action segmentation method. The method proposed in this paper uses the global temporal information captured by refinement layer to assist the Encoder-Decoder (ED) structure in judging the action segmentation point more accurately and, at the same time, suppress the excessive segmentation phenomenon caused by the ED structure. The method proposed in this paper achieves 93% frame accuracy on the constructed real Tai Chi action dataset. The experimental results prove that this method can accurately and efficiently complete the long video action segmentation task.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"290-302"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676351","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Synergistic Multi-Drug Combination Prediction Based on Heterogeneous Network Representation Learning with Contrastive Learning 基于异构网络表征学习与对比学习的多种药物协同组合预测
IF 6.6 1区 计算机科学
Tsinghua Science and Technology Pub Date : 2024-09-11 DOI: 10.26599/TST.2023.9010149
Xin Xi;Jinhui Yuan;Shan Lu;Jieyue He
{"title":"Synergistic Multi-Drug Combination Prediction Based on Heterogeneous Network Representation Learning with Contrastive Learning","authors":"Xin Xi;Jinhui Yuan;Shan Lu;Jieyue He","doi":"10.26599/TST.2023.9010149","DOIUrl":"https://doi.org/10.26599/TST.2023.9010149","url":null,"abstract":"The combination of multiple drugs is a significant therapeutic strategy that can enhance treatment effectiveness and reduce medication side effects. However, identifying effective synergistic drug combinations in a vast search space remains challenging. Current methods for predicting synergistic drug combinations primarily rely on calculating drug similarity based on the drug heterogeneous network or drug information, enabling the prediction of pairwise synergistic drug combinations. However, these methods not only fail to fully study the rich information in drug heterogeneous networks, but also can only predict pairwise drug combinations. To address these limitations, we present a novel Synergistic Multi-drug Combination prediction method of Western medicine based on Heterogeneous Network representation learning with Contrastive Learning, called SMC-HNCL. Specifically, two drug features are learnt from different perspectives using the drug heterogeneous network and anatomical therapeutic chemical (ATC) codes, and fused by attention mechanism. Furthermore, a group representation method based on multi-head self-attention is employed to learn representations of drug combinations, innovatively realizing the prediction of synergistic multi-drug combinations. Experimental results demonstrate that SMC-HNCL outperforms the state-of-the-art baseline methods in predicting synergistic drug pairs on two synergistic drug combination datasets and can also effectively predict synergistic multi-drug combinations.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"215-233"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676350","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Quantifying Bytes: Understanding Practical Value of Data Assets in Federated Learning 量化字节:了解联合学习中数据资产的实用价值
IF 6.6 1区 计算机科学
Tsinghua Science and Technology Pub Date : 2024-09-11 DOI: 10.26599/TST.2024.9010034
Minghao Yao;Saiyu Qi;Zhen Tian;Qian Li;Yong Han;Haihong Li;Yong Qi
{"title":"Quantifying Bytes: Understanding Practical Value of Data Assets in Federated Learning","authors":"Minghao Yao;Saiyu Qi;Zhen Tian;Qian Li;Yong Han;Haihong Li;Yong Qi","doi":"10.26599/TST.2024.9010034","DOIUrl":"https://doi.org/10.26599/TST.2024.9010034","url":null,"abstract":"The data asset is emerging as a crucial component in both industrial and commercial applications. Mining valuable knowledge from the data benefits decision-making and business. However, the usage of data assets raises tension between sensitive information protection and value estimation. As an emerging machine learning paradigm, Federated Learning (FL) allows multiple clients to jointly train a global model based on their data without revealing it. This approach harnesses the power of multiple data assets while ensuring their privacy. Despite the benefits, it relies on a central server to manage the training process and lacks quantification of the quality of data assets, which raises privacy and fairness concerns. In this work, we present a novel framework that combines Federated Learning and Blockchain by Shapley value (FLBS) to achieve a good trade-off between privacy and fairness. Specifically, we introduce blockchain in each training round to elect aggregation and evaluation nodes for training, enabling decentralization and contribution-aware incentive distribution, with these nodes functionally separated and able to supervise each other. The experimental results validate the effectiveness of FLBS in estimating contribution even in the presence of heterogeneity and noisy data.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"135-147"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676341","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Improve GMRACCF Qualifications via Collaborative Filtering in Vehicle Sales Chain 通过汽车销售链中的协同过滤改进 GMRACCF 资格认证
IF 6.6 1区 计算机科学
Tsinghua Science and Technology Pub Date : 2024-09-11 DOI: 10.26599/TST.2023.9010145
Beiteng Yang;Haibin Zhu;Dongning Liu
{"title":"Improve GMRACCF Qualifications via Collaborative Filtering in Vehicle Sales Chain","authors":"Beiteng Yang;Haibin Zhu;Dongning Liu","doi":"10.26599/TST.2023.9010145","DOIUrl":"https://doi.org/10.26599/TST.2023.9010145","url":null,"abstract":"The Vehicle Allocation Problem (VAP) in the vehicle sales chain has three bottlenecks in practice. The first is to collect relevant cooperation or conflict information, the second is to accurately quantify and analyze other factors affecting the distribution of cars, and the third is to establish a stable and rapid response to the vehicle allocation management method. In order to improve the real-time performance and reliability of vehicle allocation in the vehicle sales chain, it is crucial to find a method that can respond quickly and stabilize the vehicle allocation strategy. Therefore, this paper addresses these issues by extending Group Multi-Role Assignment with Cooperation and Conflict Factors (GMRACCF) from a new perspective. Through the logical reasoning of closure computation, the KD45 logic algorithm is used to find the implicit cognitive Cooperation and Conflict Factors (CCF). Therefore, a collaborative filtering comprehensive evaluation method is proposed to help administrators determine the influence weight of CCFs and Cooperation Scales (CSs) on the all-round performance according to their needs. Based on collaborative filtering, semantic modification is applied to resolve conflicts among qualifications. Large-scale simulation results show that the proposed method is feasible and robust, and provides a reliable decision-making reference in the vehicle sales chain.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"247-261"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676354","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Restage: Relation Structure-Aware Hierarchical Heterogeneous Graph Embedding Restage:关系结构感知的分层异构图嵌入
IF 6.6 1区 计算机科学
Tsinghua Science and Technology Pub Date : 2024-09-11 DOI: 10.26599/TST.2023.9010147
Huanjing Zhao;Pinde Rui;Jie Chen;Shu Zhao;Yanping Zhang
{"title":"Restage: Relation Structure-Aware Hierarchical Heterogeneous Graph Embedding","authors":"Huanjing Zhao;Pinde Rui;Jie Chen;Shu Zhao;Yanping Zhang","doi":"10.26599/TST.2023.9010147","DOIUrl":"https://doi.org/10.26599/TST.2023.9010147","url":null,"abstract":"Heterogeneous graphs contain multiple types of entities and relations, which are capable of modeling complex interactions. Embedding on heterogeneous graphs has become an essential tool for analyzing and understanding such graphs. Although these meticulously designed methods make progress, they are limited by model design and computational resources, making it difficult to scale to large-scale heterogeneous graph data and hindering the application and promotion of these methods. In this paper, we propose Restage, a relation structure-aware hierarchical heterogeneous graph embedding framework. Under this framework, embedding only a smaller-scale graph with existing graph representation learning methods is sufficient to obtain node representations on the original heterogeneous graph. We consider two types of relation structures in heterogeneous graphs: interaction relations and affiliation relations. Firstly, we design a relation structure-aware coarsening method to successively coarsen the original graph to the top-level layer, resulting in a smaller-scale graph. Secondly, we allow any unsupervised representation learning methods to obtain node embeddings on the top-level graph. Finally, we design a relation structure-aware refinement method to successively refine the node embeddings from the top-level graph back to the original graph, obtaining node embeddings on the original graph. Experimental results on three public heterogeneous graph datasets demonstrate the enhanced scalability of representation learning methods by the proposed Restage. On another large-scale graph, the speed of existing representation learning methods is increased by up to eighteen times at most.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"198-214"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676347","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Diffusion Models for Medical Image Computing: A Survey 医学影像计算的扩散模型:调查
IF 6.6 1区 计算机科学
Tsinghua Science and Technology Pub Date : 2024-09-11 DOI: 10.26599/TST.2024.9010047
Yaqing Shi;Abudukelimu Abulizi;Hao Wang;Ke Feng;Nihemaiti Abudukelimu;Youli Su;Halidanmu Abudukelimu
{"title":"Diffusion Models for Medical Image Computing: A Survey","authors":"Yaqing Shi;Abudukelimu Abulizi;Hao Wang;Ke Feng;Nihemaiti Abudukelimu;Youli Su;Halidanmu Abudukelimu","doi":"10.26599/TST.2024.9010047","DOIUrl":"https://doi.org/10.26599/TST.2024.9010047","url":null,"abstract":"Diffusion models are a type of generative deep learning model that can process medical images more efficiently than traditional generative models. They have been applied to several medical image computing tasks. This paper aims to help researchers understand the advancements of diffusion models in medical image computing. It begins by describing the fundamental principles, sampling methods, and architecture of diffusion models. Subsequently, it discusses the application of diffusion models in five medical image computing tasks: image generation, modality conversion, image segmentation, image denoising, and anomaly detection. Additionally, this paper conducts fine-tuning of a large model for image generation tasks and comparative experiments between diffusion models and traditional generative models across these five tasks. The evaluation of the fine-tuned large model shows its potential for clinical applications. Comparative experiments demonstrate that diffusion models have a distinct advantage in tasks related to image generation, modality conversion, and image denoising. However, they require further optimization in image segmentation and anomaly detection tasks to match the efficacy of traditional models. Our codes are publicly available at: https://github.com/hiahub/CodeForDiffusion.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"357-383"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676408","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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