Siamese Labels Auxiliary Learning

no code implementations • 27 Feb 2021 • Wenrui Gan, Zhulin Liu, C. L. Philip Chen, Tong Zhang

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.

Auxiliary Learning