  In deep learning, auxiliary training has been widely used to assist the
training of models. During the training phase, using auxiliary modules to
assist training can improve the performance of the model. During the testing
phase, auxiliary modules can be removed, so the test parameters are not
increased. In this paper, we propose a novel auxiliary training method, Siamese
Labels Auxiliary Learning (SiLa). Unlike Deep Mutual Learning (DML), SiLa
emphasizes auxiliary learning and can be easily combined with DML. In general,
the main work of this paper include: (1) propose SiLa Learning, which improves
the performance of common models without increasing test parameters; (2)
compares SiLa with DML and proves that SiLa can improve the generalization of
the model; (3) SiLa is applied to Dynamic Neural Networks, and proved that SiLa
can be used for various types of network structures.
