Training Data Augmentation

Data augmentation techniques enhance the training of machine learning models by artificially expanding datasets, addressing limitations like data scarcity or class imbalance. Current research focuses on developing effective augmentation strategies tailored to specific tasks, such as generating synthetic dysarthric speech for improved speech recognition or using test-time augmentation to rectify outliers in few-shot learning. These methods are proving valuable across diverse applications, including improving the robustness of harmful language detection models, enhancing the generalization of multimodal fake news detectors, and boosting the accuracy of medical diagnoses from limited electrocardiogram data. The ultimate goal is to improve model performance and generalization capabilities, particularly in resource-constrained scenarios.