Augmented Dataset

Augmenting datasets involves artificially expanding existing data to improve the performance and robustness of machine learning models, particularly when training data is scarce or imbalanced. Current research focuses on developing effective augmentation techniques tailored to specific data types (e.g., time series, tabular data, images, text) and employing various methods, including generative models like LLMs and noise injection, to create synthetic data that enhances model generalization. This is crucial for addressing challenges like class imbalance, improving model performance in low-data regimes, and mitigating biases in predictive models across diverse applications, from healthcare to remote sensing. The resulting improvements in model accuracy and fairness have significant implications for various scientific fields and real-world applications.