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1. Generative Adversarial Networks for Synthetic Meteorological Data Generation NSTL国家科技图书文献中心

Diogo Viana |  Rita Teixeira... -  《Progress in Artificial Intelligence,Part II》 -  EPIA Conference on Artificial Intelligence - 2025, - 197~206 - 共10页

摘要: data generation are compared. The model addressed in |  synthetic generation of meteorological data. With the | This study explores models for synthetic data |  generation of time series. In order to improve the achieved |  results, i.e., the data generated, new ways of
关键词: Time series |  Generative adversarial networks |  Synthetic data generation |  Meteorological data

2. Search-Based MC/DC Test Data Generation With OCL Constraints NSTL国家科技图书文献中心

Hassan Sartaj |  Muhammad Zohaib Iqba...... -  《Software testing, verification and reliability》 - 2025,35(1) - e1906.1~e1906.24 - 共24页

摘要:) criterion. Current model-based test data generation |  automate MC/DC test data generation during model-based |  test data generation using CBR, range reduction, both |  reduction for MC/DC test data generation outperform the |  and range reduction for MC/DC test data generation
关键词: model-based testing (MBT) |  modified condition/decision coverage (MC/DC) |  Object Constraint Language (OCL) |  test data generation

3. Scaling Synthetic Brain Data Generation NSTL国家科技图书文献中心

Mike Doan |  Sergey Plis -  《IEEE journal of biomedical and health informatics》 - 2025,29(2) - 840~847 - 共8页

摘要: synthetic data generation can potentially address this |  data generation for deep learning in neuroimaging | . Wirehead's architecture decouples data generation from |  data generation tool that requires 7 days to train a |  issue, on-the-fly generation is computationally
关键词: Training |  Generators |  Data collection |  Graphics processing units |  Throughput |  Computer architecture |  Synthetic data |  Neuroimaging |  Data models |  Brain modeling

4. Generate-then-Revise: An Effective Synthetic Training Data Generation Framework for Event Detection NSTL国家科技图书文献中心

Huidong Du |  Hao Sun... -  《Chinese Computational Linguistics》 -  China National Conference on Computational Linguistics - 2025, - 57~72 - 共16页

摘要:, highlighting the potential of synthetic data generation for |  event types are diverse and the annotated data is |  generate high-quality training data in three stages | , including a novel data revision step to minimize noise in |  the synthetic data. The generated data is then used
关键词: Data generation |  Event detection |  Large language model

5. Introduction to the Special Issue on Realistic Synthetic Data: Generation, Learning, Evaluation NSTL国家科技图书文献中心

Bogdan Ionescu |  Ioannis Patras... -  《ACM transactions on multimedia computing communications and applications》 - 2025,21(1) - 1.1~1.7 - 共7页

摘要: volume on Realistic Synthetic Data: Generation | , controllable generation for learning from synthetic data |  in data generation, addressing bias, limitations |  and trustworthiness in data generation, evaluation |  that relates to synthetic data for various modalities
关键词: synthetic data |  Generative Adversarial Networks |  Diffusion Models |  data augmentation |  data generation |  datasets

6. A network traffic data generation model based on AOT-DDPM for abnormal traffic detection NSTL国家科技图书文献中心

Gong, Xingyu |  Chen, Siyu... -  《Evolving Systems》 - 2025,16(1) - 共18页

摘要: traffic data. However, the existing data generation |  and improve the model's data generation performance |  of data generation, and can solve the problems of |  data generation in the field of network abnormal |  the actual detection, the imbalance of traffic data
关键词: Abnormal traffic detection |  Imbalanced data |  Diffusion model |  Data generation |  Deep learning

7. TSynD: Targeted Synthetic Data Generation for Enhanced Medical Image Classification Leveraging Epistemic Uncertainty to Improve Model Performance NSTL国家科技图书文献中心

Joshua Niemeijer |  Jan Ehrhardt... -  《Simulation and Synthesis in Medical Imaging》 -  International Workshop on Simulation and Synthesis in Medical Imaging |  International Conference on Medical Image Computing and Computer Assisted Intervention - 2025, - 69~78 - 共10页

摘要: the costly generation of data annotations, typically |  targeted generation of synthetic training data, in order | The usage of medical image data for the |  realistic synthetic data for the training process. However |  generative model to synthesize data with high epistemic
关键词: Synthetic data generation |  Generalization |  Robustness

8. Synthetic seismic data generation with pix2pix for enhanced fault detection model training NSTL国家科技图书文献中心

Choi, Byunghoon |  Pyun, Sukjoon... -  《Computers & geosciences》 - 2025,197(Mar.) - 1.1~1.18 - 共18页

摘要: synthetic data generation is crucial for effective fault | Manual fault interpretation from seismic data |  challenges, but acquiring sufficient labeled data is |  difficult and costly. Synthetic data offers a solution |  biases. It can be used alongside field data for pre
关键词: Fault detection |  pix2pix |  Optimal synthetic data generation |  Sketch-based modeling |  NEURAL-NETWORKS

9. Automatic meter reading via simulated water meter wheel rotation data generation NSTL国家科技图书文献中心

Zhao, Qianhui |  Zhu, Guanhua... -  《Measurement Science & Technology》 - 2025,36(2) - 1~14 - 共14页

摘要: attention. This paper addresses the issues of data |  generator that automatically generates water meter data |  increasing the semi-character data samples and introducing | With the rapid development of smart cities | , automated meter reading (AMR) technology, as a crucial
关键词: automatic meter reading |  data generation |  semi-character recognition |  counter extraction

10. Privacy-Preserving Tabular Data Generation: Application to Sepsis Detection NSTL国家科技图书文献中心

Eric Macias-Fassio |  Aythami Morales... -  《Pattern Recognition,Part XII》 -  International Conference on Pattern Recognition - 2025, - 75~89 - 共15页

摘要: synthetic data generation methods offers a promising |  to current synthetic data generation methodologies |  effectiveness of synthetic data generation techniques in |  Intelligence (AI) and data protection legislation, given the |  opportunity for data-driven technologies. In this study, we
关键词: Synthetic data |  Machine learning |  Sepsis detection
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