As an automated dynamic model generation framework, Crenacast is perfectly suited to efficiently derive multiple parameter-sharing models with diverse complexities and adequate predictive capabilities. A mixed search space is designed and an importance-aware progressive training scheme is proposed to prevent interference between different architectures, which avoids the model retraining and improves search efficiency, thereby efficiently deriving multiple models. Extensive experiments are conducted on datasets to demonstrate the effectiveness and efficiency of our solutions.
