Generative Blending Augmentation

Generative blending augmentation is a data augmentation technique that uses generative models, such as SinGAN, to create synthetic training data by blending features from existing images or text. This approach aims to improve the generalization ability of machine learning models, particularly in scenarios with limited data or significant domain shifts, such as cross-modal image segmentation or reinforcement learning. Current research focuses on applying this technique across diverse tasks, including semantic textual relatedness, image classification, and medical image segmentation, often in conjunction with self-training or other adaptation methods. The resulting improvements in model performance and robustness have significant implications for various fields, enhancing the reliability and applicability of machine learning models in real-world applications.