Auxiliary Loss

Auxiliary loss methods enhance deep learning model training by incorporating additional objectives beyond the primary task loss. Current research focuses on optimizing the design and integration of these auxiliary losses, exploring their impact on model generalization, robustness, and efficiency across various architectures including transformers and recurrent neural networks. This approach improves performance in diverse applications such as image processing, natural language processing, and reinforcement learning, leading to more accurate, robust, and efficient models. The effectiveness of different auxiliary loss strategies and their optimal weighting remain active areas of investigation.