Multi Loss

Multi-loss training in machine learning involves optimizing neural networks using multiple, often complementary, loss functions simultaneously. This approach aims to improve model performance by addressing various aspects of a problem, such as class imbalance, robustness to out-of-distribution data, or the need for accurate uncertainty quantification. Current research focuses on applying multi-loss strategies across diverse architectures, including convolutional neural networks, transformers, and recurrent networks, often incorporating techniques like attention mechanisms and Wasserstein gradient flows. The resulting improvements in accuracy, calibration, and efficiency have significant implications for various applications, from medical image analysis and speech enhancement to recommender systems and music transcription.