Bidirectional Learning

Bidirectional learning enhances machine learning models by processing information in both forward and backward directions, improving accuracy and robustness compared to unidirectional approaches. Current research focuses on applying this concept to various tasks, including sequence-to-sequence modeling, tabular data analysis, and image/video processing, often employing architectures like transformers and recurrent neural networks with techniques such as mutual learning and dual teacher-student frameworks. These advancements lead to improved performance in diverse applications, ranging from natural language processing and computer vision to autonomous reinforcement learning and 3D reconstruction. The resulting models demonstrate increased efficiency and accuracy, particularly when dealing with noisy data or limited supervision.