Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs
Istanbul Technical University（伊斯坦布尔理工大学）；ITU, AI Research and Application Center（伊斯坦布尔理工大学AI研究与应用中心）；ITU, Department of Computer Engineering（伊斯坦布尔理工大学计算机工程系）；BTS Group（BTS集团）；Gazi University（加齐大学）
Graphs are crucial for representing interrelated data and aiding predictive modeling by capturing complex relationships. Achieving high-quality graph representation is important for identifying linked patterns, leading to improvements in Graph Neural Networks (GNNs) to better capture data structures. However, challenges such as data scarcity, high collection costs, and ethical concerns limit progress. As a result, generative models and data augmentation have become more and more popular. This study explores using generated graphs for data augmentation, comparing the performance of combining generated graphs with real graphs, and examining the effect of different quantities of generated graphs on graph classification tasks. The experiments show that balancing scalability and quality requires different generators based on graph size. Our results introduce a new approach to graph data augmentation, ensuring consistent labels and enhancing classification performance.
Synthetic Augmentation in Imbalanced Learning: When It Helps, When It Hurts, and How Much to Add
Department of Electrical & Computer Engineering, Duke University（电气与计算机工程系，杜克大学）；Department of Biostatistics & Bioinformatics and Department of Computer Science, Duke University（生物统计学与生物信息学系和计算机科学系，杜克大学）
When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification?
Department of Electrical & Computer Engineering, Duke University（电气与计算机工程系，杜克大学）；Department of Biostatistics & Bioinformatics, Duke University（生物统计学与生物信息学系，杜克大学）；Department of Biostatistics & Bioinformatics and Department of Computer Science, Duke University（生物统计学与生物信息学系和计算机科学系，杜克大学）
Stylized Synthetic Augmentation further improves Corruption Robustness
Institute of Industrial Automation and Software Engineering, University of Stuttgart, Stuttgart, Germany（工业自动化与软件工程学院，斯图加特大学，斯图加特，德国）；Federal Institute for Occupational Safety and Health (BAuA)（联邦职业安全与健康研究所）
Synthetic Source Language Augmentation for Colloquial Neural Machine Translation
Kata.ai Research Team（Kata.ai研究团队）；Sekolah Tinggi Informatika & Komputer Indonesia (STIKI)（印度尼西亚信息与计算机高等学院（STIKI））；Beijing Institute of Technology（北京理工大学）；Faculty of Computer Science, Universitas Indonesia（印度尼西亚大学计算机学院）
Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models
XITASO GmbH（XITASO 有限公司）；Zuse School of Excellence in Reliable AI（Zuse 卓越可靠人工智能学院）；Technische Hochschule Ingolstadt（因戈尔施塔特应用技术大学）；Technische Hochschule Augsburg（奥格斯堡应用技术大学）
Copula-based synthetic data augmentation for machine-learning emulators
University of Reading（雷丁大学）；Imperial College London（帝国理工学院）；Leiden University（莱顿大学）；European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）
我们能用合成数据改进机器学习（ML）仿真器吗？如果数据稀缺或来源昂贵，并且物理模型可用，则统计生成的数据可以用于廉价地扩充训练集。在这里，我们探讨了使用基于Copula的模型生成综合增强数据集的天气和气候测试的玩具物理模型的下降流长波辐射和相应的神经网络仿真器的方法。结果表明，Copula增强的数据集，预测提高了62%的平均绝对误差（从1.17到0.44 W m $^{-2}$）。
Online Data Augmentation for Forecasting with Deep Learning
Faculdade de Engenharia da Universidade do Porto（波尔图大学工程学院）；Laboratory for Artificial Intelligence and Computer Science (LIACC)（人工智能与计算机科学实验室）；INESC TEC；Fraunhofer Portugal AICOS（弗劳恩霍夫葡萄牙AICOS）
Should I use Synthetic Data for That? An Analysis of the Suitability of Synthetic Data for Data Sharing and Augmentation
Lausanne University Hospital（拉索纳大学医院）；Swiss Data Science Center, EPFL（瑞士数据科学中心，EPFL）；University of Lausanne（洛桑大学）；Max Planck Institute for Security & Privacy EPFL（马克斯·普朗克安全与隐私研究所 EPFL）
Accelerating Dataset Distillation via Model Augmentation
Zhejiang University（浙江大学）；Texas A&M University（德州农工大学）；Microsoft Research（微软研究院）；New York University（纽约大学）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）；University of Connecticut（康涅狄格大学）；University of North Carolina, Chapel Hill（北卡罗来纳大学教堂山分校）；North Carolina State University（北卡罗来纳州立大学）
SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation in Melanoma Diagnosis
Alliance School of Advanced Computing, Alliance University, India（印度联盟大学高级计算学院）；Department of Computer Science and Communication, Østfold University College, 1783 Halden, Norway（挪威东福尔大学学院计算机科学与传播系）；Department of Dentistry and Oral Health, Aarhus University, 8000 Aarhus, Denmark（丹麦奥胡斯大学牙科与口腔健康系）
CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation
Department of Computer Science, University of Copenhagen（哥本哈根大学计算机科学系）；Center for Information and Language Processing (CIS), LMU Munich（慕尼黑大学信息与语言处理中心）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
Solving the Class Imbalance Problem Using a Counterfactual Method for Data Augmentation
School of Computer Science, University College Dublin（都柏林大学学院计算机科学学院）；Insight Centre for Data Analytics, University College Dublin（都柏林大学学院洞察数据分析中心）；VistaMilk SFI Research Centre, University College Dublin（都柏林大学学院VistaMilk SFI研究中心）
