Knowledge and data integration for deep learning under small data - Teo, Hazel Kai Xin (Nanyang Technological University, 2024)
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.