Self Augmentation

Self-augmentation is a data augmentation technique that leverages a model's own internal representations or outputs to generate synthetic training data, thereby improving model performance and robustness. Current research focuses on applying self-augmentation to diverse machine learning tasks, including image classification, object detection, natural language processing, and time-series forecasting, often integrating it with contrastive learning or other augmentation methods. This approach addresses limitations in data quantity and quality, enhancing model generalization and reducing overfitting, with significant implications for various fields, particularly those dealing with limited or noisy datasets like medical image analysis and low-resource language processing.