MGE: A Training-Free and Efficient Model Generation and Enhancement
  Scheme
Xuan Wang Zeshan Pang Yuliang Lu Xuehu Yan
To provide a foundation for the research of deep learning models, the
construction of model pool is an essential step. This paper proposes a
Training-Free and Efficient Model Generation and Enhancement Scheme (MGE). This
scheme primarily considers two aspects during the model generation process: the
distribution of model parameters and model performance. Experiments result
shows that generated models are comparable to models obtained through normal
training, and even superior in some cases. Moreover, the time consumed in
generating models accounts for only 1\% of the time required for normal model
training. More importantly, with the enhancement of Evolution-MGE, generated
models exhibits competitive generalization ability in few-shot tasks. And the
behavioral dissimilarity of generated models has the potential of adversarial
defense.