A Novel Training Protocol for Performance Predictors of Evolutionary Neural Architecture Search Algorithms

Understand

Evolutionary Neural Architecture Search (ENAS) can automatically design the architectures of Deep Neural Networks (DNNs) using evolutionary computation algorithms.

• However, most ENAS algorithms require intensive computational resource, which is not necessarily available to the users interested.
• Performance predictors are a type of regression models which can assist to accomplish the search, while without exerting much computational resource.
• Despite various performance predictors have been designed, they employ the same training protocol to build the regression models: 1) sampling a set of DNNs with performance as the training dataset, 2) training the model with the mean square error criterion, and 3) predicting the performance of DNNs newly generated during the ENAS.