 Our approach can create parameter efficient networks by using a relatively small number of weights, or can improve a model's performance by adding additional model capacity during training without affecting the computational resources required at test time.
<coherence>  Our approach can create parameter efficient networks by using a relatively small number of weights, or can improve a model's performance by adding additional model capacity during training without affecting the computational resources required at test time.