Model Augmentation

Model augmentation enhances machine learning models by strategically modifying either the training data or the model architecture itself to improve performance and robustness. Current research focuses on developing augmentation techniques tailored to specific model types and application domains, including generative models for image classification, contrastive learning for graph data, and reinforcement learning for improved human-in-the-loop training. These advancements address challenges like data scarcity, covariate shift, and the need for more efficient training, ultimately leading to more accurate, reliable, and generalizable models across diverse fields.