Joint Training

Joint training, a technique that simultaneously optimizes multiple models or tasks within a single training process, aims to improve model performance and efficiency. Current research focuses on applying joint training to diverse areas, including recommender systems, EEG analysis, and speech and image processing, often employing techniques like self-supervised learning, reinforcement learning, and multi-task learning with various neural network architectures (e.g., CNNs, Transformers). This approach holds significant promise for enhancing model robustness, reducing computational costs, and improving performance in data-scarce or noisy environments across numerous applications.