带有半渐进式分层提取机制的轻量化多任务模型OACSTPCD

Currently,the multi-tasking learning model is widely used in many fields.However,most models with better effect consist of complex network layers and architectures,which makes it difficult for these multi-task learning models to be applied to resource-limited devices,for example,countries or regions with limited funds but large population bases carry out census prediction,portable devices carry out translation activities and other tasks.In view of the above,a lightweight multi-tasking learning model with semi-progressive layered extraction mechanism is proposed.In the model,the top-level Expert module of the specific task is pruned first and the work originally responsible for extracting the in-depth information of each specific task is entrusted to the Tower module of each task.This approach makes the model lightweight and,at the same time,retains the characteristics of separating the shared parameters of the task and the unique parameters of the task and extracting information hierarchically.Inspired by uncertainty to weigh losses,the dynamic joint loss is optimized to compensate for the decrease in the performance and accuracy of the model after pruning.The model can predict the importance of tasks continuously and adjust the weight of each task.Some hyperparameters are also tuned.The evaluation of the model on the UCI Census-Income public dataset finally proves that the model has the same performance as that before lightweight.