Auxiliary Network

Auxiliary networks are supplementary neural network components designed to improve the training or performance of primary networks. Current research focuses on using auxiliary networks for diverse tasks, including addressing class imbalance in segmentation, enhancing local learning efficiency in large models, and improving the robustness and interpretability of deep learning models. These techniques aim to overcome limitations of traditional end-to-end training, such as high memory consumption and difficulties in parallelization, leading to more efficient and effective deep learning systems across various applications like image classification, segmentation, and even brain network analysis. The resulting improvements in training speed, accuracy, and interpretability have significant implications for both fundamental research and practical applications in diverse fields.