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Latest Technologies on dataset Distillation: A Survey

Procedia Computer Science 2024年 242卷 1112-1117页

作者： Muyang Li Yi Qu Yong Shi Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 USA

dataset distillation refers to the process of constructing a smaller dataset based on a larger dataset, so that the training model with the smaller dataset can obtain similar results to the larger dataset. In this paper, we first briefly review the basic methods and some recent advances in this field from the perspectives of meta-learning framework and data matching framework, including the backpropagation through time(BPTT), kernel ridge regression(KRR), decoupled techniques, gradient matching(DC), trajectory matching(MTT) and distribution matching(DM). The results of the plug-and-play approach in recent years are then presented. Finally, we discuss possible future developments from the perspectives of data distillation algorithms on large datasets, the relationship between compress ratio and information contained, more tasks and data modalities, and the use of generative models to improve the distillation results of datasets.

关键词： dataset Distillation Survey Meta-learning framework data matching framework

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Impact of the China Railway Express on Employment Structure - Evidence from Urban Economic data

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Procedia Computer Science 2024年 242卷 749-757页

作者： Bochao Yan Hailiang Jiang Junfeng Wu Xin Tian China Agricultural University Beijing 100083 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Shanghai WinJoin Information Technology Co. Ltd. Shanghai 200126 China

As the main international trade channel of China’s “westward development”, the opening of China Railway Express has significantly improved the trade facilitation of countries along the route. This paper investigates the impact of the opening of China Railway Express sites on the employment choices of individuals by using the difference in difference method based on the economic panel data of China’s prefecture-level cities and the data on the opening of China Railway Express during the period of 2007~2019, which are matched by the correlation. The results of the study show that the opening of the China Railway Express increases the probability of individuals within the radiation area to enter the private sector for employment. The empirical results provide a new explanatory mechanism for the opening of the China Railway Express on economic growth, which has certain theoretical and policy value.

关键词： China Railway Express The Belt Road Employment Structure Quasi-natural Experiments

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A Brief Survey of Distribution Robust Graph Neural Networks

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Procedia Computer Science 2024年 242卷 1281-1286页

作者： Lei Zheng Pei Quan Yong Shi Lingfeng Niu The School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China The College of Economics and Management Beijing University of Technology Beijing 100124 China The School of Economics and Management University of Chinese Academy of Sciences Beijing 1001090 China

Graph neural network is a powerful tool for solving various graph tasks, such as node classification and graph classification. However, there is increasing evidence suggesting that it is sensitive to distribution shifts and its performance is reduced by at least 10% when distribution shifts happen. In the last three years, to solve the wide existing distribution shifts, some distribution robust strategies have been designed specifically for graph neural networks. It is also one of the popular research topics in graph neural networks in recent years. Therefore, in this paper, we divide these strategies into three categories depending on the specific problems they solve: distribution shift for semi-supervised node classification, distribution shift for unsupervised node classification, and out-of-distribution shift for graph classification. Furthermore, we discuss and summarize the strategies for each type of problem. After investigating these strategies, we put forward future development direction.

关键词： Graph neural networks Shift robust Distribution generalization

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Image Orientation Estimation Based On Deep Learning - A Survey

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Procedia Computer Science 2024年 242卷 1193-1197页

作者： Ruijie Xu Yong Shi Zhiquan Qi Research Center on Fictitious Economy and Data Science University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing 00190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska Omaha 68182 USA

In the realm of visual tasks, the estimation of orientation plays a pivotal role yet often remains an understudied aspect in the gamut of image analysis research. This paper endeavors to address this deficiency by conducting a comprehensive review of methods for orientation estimation within 2D imaging contexts. We aim to provide researchers with an exhaustive overview and detailed analysis of the techniques utilized for orientation estimation, thereby enhancing their understanding of this complex field. We introduce an organized classification of orientation estimation methods, segmented into approaches based on classification algorithms and those based on regression models. This classification not only sheds light on the distinct methodologies but also scrutinizes their underlying assumptions, practical applications. Additionally, the study identifies and underscores emerging research opportunities that hold the potential to enrich the discourse and technical advancements in orientation estimation, thereby encouraging deeper academic investigation and technological development.

关键词： Classification-based Orientation Estimation Regression-based Orientation Estimation

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When liver disease diagnosis encounters deep learning:Analysis,challenges,and prospects

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iLIVER 2023年第1期2卷 73-87页

作者： Yingjie Tian Minghao Liu Yu Sun Saiji Fu School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of SciencesBeijing 100190China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS Beijing 100190China Tsinghua University Press Beijing 102611China School of Economics and Management Beijing University of Posts and TelecommunicationsBeijing 100876China

The liver is the second-largest organ in the human body and is essential for digesting food and removing toxic ***,obesity,alcohol use,and other factors can damage the liver and cause liver *** diagnosis of liver disease used to depend on the clinical experience of doctors,which made it subjective,difficult,and *** learning has made breakthroughs in various fields;thus,there is a growing interest in using deep learning methods to solve problems in liver research to assist doctors in diagnosis and *** this paper,we provide an overview of deep learning in liver research using 139 papers from the last 5 *** also show the relationship between data modalities,liver topics,and applications in liver research using Sankey diagrams and summarize the deep learning methods used for each liver topic,in addition to the relations and trends between these ***,we discuss the challenges of and expectations for deep learning in liver research.

关键词： Liver Deep learning data modality Liver topic Application

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Structure Aware Loss for Deep Unsupervised Industrial Image Registration

Structure Aware Loss for Deep Unsupervised Industrial Image ...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Yong Shi Mengjin Lyu Zhiquan Qi Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China College of Information Science and Technology University of Nebraska at Omaha Omaha USA School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

Image registration is extensively utilized in industrial applications. However, specific challenges inherent to these environments—such as low texture, significant displacement, and poor lighting conditions—complicate the registration process. This paper introduces a structure-aware loss module tailored to the unique characteristics of industrial images. This module promotes the network’s focus on salient regions while emphasizing structural features. Additionally, it is designed as a plug-in component, facilitating integration with any loss function and homography estimation method. We develop an unsupervised registration network and create an industrial dataset to evaluate the module’s effectiveness. A series of experiments demonstrate that our structure-aware module significantly enhances registration performance.

关键词： Deep learning Image registration Conferences Lighting Estimation data mining

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Optimization Strategies for Client Drift in Federated Learning: A review 9th

Optimization Strategies for Client Drift in Federated Learni...

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9th International Conference on Information technology and Quantitative Management, ITQM 2022

作者： Shi, Yong Zhang, Yuanying Xiao, Yang Niu, Lingfeng School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Sciences and Technology University of Nebraska at Omaha NE68182 United States School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100190 China Faculty of Information Technology Beijing University of Technology Beijing100124 China

With the development of AI technology, there is an increasing awareness and concern about data privacy, and training data is becoming more and more fragmented. To make better use of this distributed data while ensuring privacy, federated learning is proposed, which can federate multiple distributed devices to train a machine learning model and does not exchange the raw training data. A key challenge in federated learning is that the distribution of data across clients is heterogeneous, which leads to client drift, that is, local model updates toward local optimal solutions, resulting in performance degradation of the global model. To address this problem, many federated learning algorithms have been proposed. In this paper, we provide a review of existing federated learning optimization strategies. In our opinion, the existing optimization strategies for client drift can be roughly classified into the following two categories: model aggregation and model training. We introduce these two categories separately. Finally, we present our perspectives on the future development. © 2022 The Author(s).

关键词： data privacy

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Impact of Consumer Characteristics on IP Product Purchase: Empirical Evidence from a Chinese Chain Retailer 10

Impact of Consumer Characteristics on IP Product Purchase: E...

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10th International Conference on Information technology and Quantitative Management, ITQM 2023

作者： Tang, Jiayu Wang, Tingyan Tian, Xin Cai, Qiang Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Sino-Danish College University of Chinese Academy of Sciences Beijing100190 China Nuffield Department of Medicine University of Oxford South Parks Road OxfordOX1 3SY United Kingdom NIHR Oxford Biomedical Research Centre Big Data Institute University of Oxford Old Road Campus OxfordOX3 7LF United Kingdom School of Economics and Management Ningbo University of Technology Ningbo315211 China

Developing IP product has become a common differentiation strategy for retailers to attract customers and remain competence strength. This study uses empirical model to relate consumer characteristics with IP product purchase. The results show that consumers shopping on weekends are more likely to buy IP products, and consumers who have stocking behavior are less likely to purchase IP products. Consumers who are members of the retailer also have lower probability to buy IP products. These findings provide suggestions for the retailers who want to apply differentiation strategy by introducing co-branded IP products. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information technology and Quantitative Management.

关键词： Consumer behavior

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Overview of Essential Components in deep learning reference-based super resolution methods

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Procedia Computer Science 2024年 242卷 1243-1248页

作者： Jiayu Xue Junjie Liu Yong Shi School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 101408 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Sino-Danish College University of Chinese Academy of Sciences Beijing 100049 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 USA

Reference-based super resolution (RefSR) aims to recover the lost details in a low-resolution image and generate a high-resolution result, guided by a high-resolution reference image with similar contents or textures. In contrast to the traditional single-image super-resolution, which focuses on the intrinsic properties of the single low-resolution image, the challenge of RefSR lies in matching and aggregating highly-related but misaligned reference images with low-resolution images. Several effective but complex designs have been proposed to address this challenge, which poses difficulties in implementing RefSR in real-world applications. In order to better understand the working mechanism of RefSR and design a more efficient and lightweight architecture, we provide a review about the essential components of the existing deep learning-based RefSR. We decompose and classify the common pipeline into four submodules according to the functionalities. Then, we summarize and describe the implementation details of the commonly-adopted approaches in each submodule. Finally, we discuss the challenges and promising research directions.

关键词： Reference-based super resolution Deep learning Feature matching Feature alignment

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Multi-view Feature Augmentation with Adaptive Class Activation Mapping

arXiv

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arXiv 2022年

作者： Gao, Xiang Tian, Yingjie Qi, Zhiquan School of Computer Science and Technology University of Chinese Academy of Sciences China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences China

We propose an end-to-end-trainable feature augmentation module built for image classification that extracts and exploits multi-view local features to boost model performance. Different from using global average pooling (GAP) to extract vectorized features from only the global view, we propose to sample and ensemble diverse multi-view local features to improve model robustness. To sample class-representative local features, we incorporate a simple auxiliary classifier head (comprising only one 1×1 convolutional layer) which efficiently and adaptively attends to class-discriminative local regions of feature maps via our proposed AdaCAM (Adaptive Class Activation Mapping). Extensive experiments demonstrate consistent and noticeable performance gains achieved by our multi-view feature augmentation module. Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

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