检索结果-内蒙古大学图书馆

Proceedings of the 38th International Conference on Neural information processing Systems

作者： Jun Dan Weiming Liu Chunfeng Xie Hua Yu Shunjie Dong Yanchao Tan Zhejiang University Queen Mary University of London Dalian University of Technology Shanghai Jiao Tong University Fuzhou University and Engineering Research Center of Big Data Intelligence Ministry of Education and Fujian Key Laboratory of Network Computing and Intelligent Information Processing

ISBN: (纸本)9798331314385

Semi-supervised graph domain adaptation, as a branch of graph transfer learning, aims to annotate unlabeled target graph nodes by utilizing transferable knowledge learned from a label-scarce source graph. However, most existing studies primarily concentrate on aligning feature distributions directly to extract domain-invariant features, while ignoring the utilization of the intrinsic structure information in graphs. Inspired by the significance of data structure information in enhancing models' generalization performance, this paper aims to investigate how to leverage the structure information to assist graph transfer learning. To this end, we propose an innovative framework called TFGDA. Specially, TFGDA employs a structure alignment strategy named STSA to encode graphs' topological structure information into the latent space, greatly facilitating the learning of transferable features. To achieve a stable alignment of feature distributions, we also introduce a SDA strategy to mitigate domain discrepancy on the sphere. Moreover, to address the overfitting issue caused by label scarcity, a simple but effective RNC strategy is devised to guide the discriminative clustering of unlabeled nodes. Experiments on various benchmarks demonstrate the superiority of TFGDA over SOTA methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution

arXiv

引用

arXiv 2022年

作者： Cheng, Ri Sun, Yuqi Yan, Bo Tan, Weimin Ma, Chenxi School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Recent multi-view multimedia applications struggle between high-resolution (HR) visual experience and storage or bandwidth constraints. Therefore, this paper proposes a Multi-View Image Super-Resolution (MVISR) task. It aims to increase the resolution of multiview images captured from the same scene. One solution is to apply image or video super-resolution (SR) methods to reconstruct HR results from the low-resolution (LR) input view. However, these methods cannot handle large-angle transformations between views and leverage information in all multi-view images. To address these problems, we propose the MVSRnet, which uses geometry information to extract sharp details from all LR multi-view to support the SR of the LR input view. Specifically, the proposed Geometry-Aware Reference Synthesis module in MVSRnet uses geometry information and all multi-view LR images to synthesize pixel-aligned HR reference images. Then, the proposed Dynamic High-Frequency Search network fully exploits the high-frequency textural details in reference images for SR. Extensive experiments on several benchmarks show that our method significantly improves over the state-of-the-art approaches. © 2022, CC BY.

关键词： Geometry

来源：评论

学校读者我要写书评

暂无评论

Restricted Arc-Connectivity of Unidirectional Star Graphs

SSRN

引用

SSRN 2022年

作者： Zhao, Xiaojun Deng, Qingying Wang, Zhiyi Key Laboratory of Intelligent Computing & Information Processing of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China

The restricted arc-connectivity is an effective assessment of the reliability of directed networks, which is an extended notion of arc-connectivity. An arc set S of D is a restricted arc-cut of D if D−S has a strong connected component D′ such that |V (D′)| ≥ 2 and D−V (D′) contains an arc. D is called λ′-connected if there exists a restricted arc-cut in D. The restricted arc-connectivity λ′(D) of a λ′-connected digraph D is the minimum cardinality over all restricted arc-cuts. We can get a unidirectional star graph by orienting the star graph with Day-Tripathi orientation. In this paper, we first show that the restricted arc-connectivity of the n-dimensional unidirectional star graph is n−2 when n is odd and n−3 when n is even for n ≥ 4. As a consequence, we prove that unidirectional star graph is super-λ. © 2022, The Authors. All rights reserved.

关键词： Graph algorithms

来源：评论

学校读者我要写书评

暂无评论

Exploiting Robust Model Watermarking Against the Model Fine-Tuning Attack via Flat Minima Aware Optimizers

Exploiting Robust Model Watermarking Against the Model Fine-...

引用

International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Dongdong Lin Yue Li Bin Li Jiwu Huang Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China SZU-AFS Joint Innovation Center for AI Technology Shenzhen University Shenzhen China Xiamen Key Laboratory of Data Security and Blockchain Technology Huaqiao University Xiamen China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the rapid advancement of deep neural networks (DNNs), model watermarking has emerged as a widely adopted technique for safeguarding model copyrights. A prevalent method involves utilizing a watermark decoder to retrieve watermark bits from generated outputs, but such methods are often vulnerable to model fine-tuning attacks. Traditionally, this challenge is mitigated through adversarial training or data augmentation, both of which significantly increase the computational burden. In this paper, we present a solution employing Flat Minima Aware (FMA) optimizers to bolster the robustness of model watermarking without requiring additional training data. By optimizing the watermark loss with flat minima awareness, our approaches significantly enhance the robustness of watermarks against the model fine-tuning attack. Comprehensive experiments have demonstrated our method’s superior ability to preserve watermark integrity. These findings suggest that this innovative optimization strategy offers a robust and efficient pathway for protecting models, thereby contributing to more secure and reliable model copyright protection mechanisms.

关键词： Training Resistance Computational modeling Training data Watermarking Signal processing Diffusion models Robustness Speech processing Optimization

来源：评论

学校读者我要写书评

暂无评论

Viterbi Decoding of Directed Acyclic Transformer for Non-Autoregressive Machine Translation

arXiv

引用

arXiv 2022年

作者： Shao, Chenze Ma, Zhengrui Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Non-autoregressive models achieve significant decoding speedup in neural machine translation but lack the ability to capture sequential dependency. Directed Acyclic Transformer (DA-Transformer) was recently proposed to model sequential dependency with a directed acyclic graph. Consequently, it has to apply a sequential decision process at inference time, which harms the global translation accuracy. In this paper, we present a Viterbi decoding framework for DA-Transformer, which guarantees to find the joint optimal solution for the translation and decoding path under any length constraint. Experimental results demonstrate that our approach consistently improves the performance of DA-Transformer while maintaining a similar decoding speedup. Copyright © 2022, The Authors. All rights reserved.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

Multi-Granularity Feature Distillation Learning Network for Fine-Grained Visual Classification

Multi-Granularity Feature Distillation Learning Network for ...

引用

Image processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Yuhang Cai Xiao Ke Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou Fujian China

ISBN: (纸本)9781665464697

Fine-grained visual classification (FGVC) aims to identify objects belonging to multiple sub-categories of the same super-category (such as species of birds, models of cars and aircraft). The key to solving fine-grained classification problems is to learn discriminative visual feature representation with only subtle differences. Although previous work based on refined feature learning has made great progress, however, high-level semantic features often lack key information for fine-grained visual object nuances. How to efficiently integrate semantic information of different granularities from classification networks is a Critical. In this paper, we propose Multi-Granularity Feature Distillation Learning Network (MGFDL-Net). Our solution integrates multi-granularity hierarchical information through a multi-granularity fusion learning strategy to enhance feature representation. In view of the inherent challenges of large intraclass differences in FGVC, a cross-layer self-distillation regularization is proposed to strengthen the connection between high-level semantics and low-level semantics for robust multi-granularity feature learning. Comprehensive experiments show that our method achieves state-of-the-art performance on three challenging fine-grained visual classification FGVC datasets (CUB-200-2011, Stanford Cars and FGVC-Aircraft).

关键词： Representation learning Visualization Cross layer design Computer vision Image processing Semantics Birds

来源：评论

学校读者我要写书评

暂无评论

Modality-specific Adaptive Scaling Method for Cross-modal Retrieval

Modality-specific Adaptive Scaling Method for Cross-modal Re...

引用

Image processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Baitao Chen Xiao Ke Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou Fujian China

ISBN: (纸本)9781665464697

There are huge differences in data distribution and feature representation of different modalities. How to flexibly and accurately retrieve data from different modalities is a challenging problem. The mainstream common subspace method only focus on the heterogeneity gap between modalities, and use a unified method to jointly learn the common representation of different modalities, which can easily lead to the difficulty of multi-modal unified fitting. In this work, we innovatively propose the concept of multi-modal information density discrepancy, and propose a modality-specific adaptive scaling method incorporating prior knowledge, which can adaptively learn the most suitable network for different modalities. Comprehensive experimental results on three widely used cross-modal retrieval datasets show the proposed MASM achieves the state-of-the-art results and significantly outperforms other existing methods.

关键词： Knowledge engineering Computer vision Adaptive systems Image processing Fitting Machine learning

来源：评论

学校读者我要写书评

暂无评论

Design of Planar Inverted-F Antenna (PIFA) for 5G Communication Indoor Micro-base station 13

Design of Planar Inverted-F Antenna (PIFA) for 5G Communicat...

引用

13th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2021

作者： Yang, Zhourui Wu, Bo Kong, Meng Zhang, Liang Li, Xianghui Wu, Xianliang Ministry Of Education Anhui University Key Laboratory Of Intelligent Computing Signal Processing Hefei230039 China Hefei Normal University Anhui Province Key Laboratory Of Simulation And Design For Electronic Information System Hefei230601 China

ISBN: (纸本)9781665434379

In this paper, a planar inverted F antenna is designed for indoor distributed micro-base station of 5G communication. The overall size of the antenna is 16 mm × 15.3 mm × 10 mm, and the antenna works in the 3.5 - 3.6 GHz, which band number named n78. The antenna has the advantages of miniaturization, easy machining and convenient installation. The simulation results of ANSYS HFSS 2020 shows that in the working bandwidth range, when the central frequency of the designed antenna is 3.55 GHz, the S11 is -23.6 dB, and the VSWR is 1.1, which meets the requirements of impedance matching. In terms of directivity, the antenna coverage also meets the requirements of 5G micro-base station. © 2021 IEEE.

关键词： Base stations

来源：评论

学校读者我要写书评

暂无评论

Computation Offloading Algorithm Based on Deep Reinforcement Learning and Multi-Task Dependency for Edge computing 25th

Computation Offloading Algorithm Based on Deep Reinforcemen...

引用

25th International Computer Symposium on New Trends in Computer Technologies and Applications, ICS 2022

作者： Lin, Tengxiang Lin, Cheng-Kuan Chen, Zhen Cheng, Hongju College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan

ISBN: (纸本)9789811995811

Edge computing is an emerging promising computing paradigm that brings computation and storage resources to the network edge, significantly reducing service latency. In this paper, we aim to divide the task into several sub-tasks through its inherent interrelation, guided by the idea of high concurrency for synchronization, and then offload sub-tasks to other edge servers so that they can be processed to minimize the cost. Furthermore, we propose a DRL-based Multi-Task Dependency Offloading Algorithm (MTDOA) to solve challenges caused by dependencies between sub-tasks and dynamic working scenes. Firstly, we model the Markov decision process as the task offloading decision. Then, we use the graph attention network to extract the dependency information of different tasks and combine Long Short-term Memory (LSTM) with Deep Q Network (DQN) to deal with time-dependent problems. Finally, simulation experiments demonstrate that the proposed algorithm boasts good convergence ability and is superior to several other baseline algorithms, proving this algorithm’s effectiveness and reliability. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer

arXiv

引用

arXiv 2022年

作者： Nan, Qiong Wang, Danding Zhu, Yongchun Sheng, Qiang Shi, Yuhui Cao, Juan Li, Jintao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Both real and fake news in various domains, such as politics, health, and entertainment are spread via online social media every day, necessitating fake news detection for multiple domains. Among them, fake news in specific domains like politics and health has more serious potential negative impacts on the real world (e.g., the infodemic led by COVID-19 misinformation). Previous studies focus on multi-domain fake news detection, by equally mining and modeling the correlation between domains. However, these multi-domain methods suffer from a seesaw problem: the performance of some domains is often improved at the cost of hurting the performance of other domains, which could lead to an unsatisfying performance in specific domains. To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains. To transfer coarse-grained domain-level knowledge, we train a general model with data of all domains from the meta-learning perspective. To transfer fine-grained instance-level knowledge and adapt the general model to a target domain, we train a language model on the target domain to evaluate the transferability of each data instance in source domains and re-weigh each instance’s contribution. Offline experiments on two datasets demonstrate the effectiveness of DITFEND. Online experiments show that DITFEND brings additional improvements over the base models in a real-world scenario. Copyright © 2022, The Authors. All rights reserved.

关键词： Social networking (online)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：