Consistency Training

Consistency training is a semi-supervised learning technique aiming to improve model robustness and generalization by enforcing consistent predictions across different data augmentations or model variations. Current research focuses on applying this technique to diverse models, including diffusion models, transformers, and various neural networks for tasks such as image generation, object detection, and natural language processing. This approach is significant because it leverages unlabeled data to enhance model performance, reducing the reliance on expensive and time-consuming data annotation, and improving generalization to unseen data distributions. The resulting improvements in accuracy and efficiency have broad implications across numerous applications.