Interpolation Based Data Augmentation

Interpolation-based data augmentation is a technique that generates synthetic training data by linearly combining existing examples and their labels, aiming to improve model robustness and generalization, particularly in data-scarce scenarios. Recent research focuses on adapting this approach to various domains, including speech-to-text, natural language processing (NLP), and object detection, with algorithms like Mixup and its variants (e.g., SegMix, MultiMix, LossMix) being developed to address specific challenges posed by different data structures and task complexities. These methods show promise in enhancing model performance across diverse applications, especially in low-resource settings, by creating more diverse and representative training datasets.