Before revision:  Second , progressive training optimizes randomly sampled subnets that inherit the weights of the supernet . Third, three search methods, including manual grid search, random search, and model predictive evolutionary search, are introduced to find a trade-off between accuracy and efficiency.
After revision:  Second , progressive training , and architecture search. Firstly, we borrow the design of TDNN to construct a supernet that enables sampling subnets with different depth, kernel, and efficiency.