Knowledge and data integration for deep learning under small data
This research addresses limited training data in deep learning, where data volume, quality, and diversity significantly influence model performance. The availability of diverse and abundant data is crucial for effective training models. However, in many real-world scenarios, obtaining such varied da...
Saved in:
| 主要作者: | |
|---|---|
| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2024
|
| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/176309 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | This research addresses limited training data in deep learning, where data volume, quality, and diversity significantly influence model performance. The availability of diverse and abundant data is crucial for effective training models. However, in many real-world scenarios, obtaining such varied data can be challenging, potentially leading to biased models, particularly affecting minority classes. Recent literature and research by various scholars emphasize data augmentation techniques as a promising solution to mitigate data scarcity and enhance model accuracy without exhaustive labeling efforts.
This study explores the potential of data augmentation, particularly text augmentation, in alleviating the dependency on extensive training data. The aim is to enhance the effectiveness and accuracy of deep learning models, especially in the context of natural language processing (NLP). We investigate the benefits of employing synonym replacement as a primary text augmentation technique, assessing its ability to generate supplementary data and improve model performance. |
|---|