Bidirectional Training

Bidirectional training involves training models to process information in both forward and reverse directions, improving performance by leveraging contextual information from both ends. Current research focuses on applying this technique to diverse tasks, including machine translation, image retrieval, and semantic segmentation, often incorporating self-training or dual-teacher approaches to enhance model robustness and generalization. This methodology shows promise in improving accuracy and efficiency across various machine learning applications, particularly in scenarios with limited labeled data or significant domain shifts, leading to advancements in fields like natural language processing and computer vision.