Network Architecture Search and specifically Regularized Evolution is a common way to refine the structure of a deep learning model.However, little is known about how models empirically evolve over time which has design implications for designing caching policies, refining the search algorithm for particular applications, and other important use cases.In this work, we algorithmically analyze and quantitatively characterize the patterns of model evolution for a set of models from the Candle project and the Nasbench-201 search space.We show how the evolution of the model structure is influenced by the regularized evolution algorithm. We describe how evolutionary patterns appear in distributed settings and opportunities for caching and improved scheduling. Lastly, we describe the conditions that affect when particular model architectures rise and fall in popularity based on their frequency of acting as a donor in a sliding window.

对于Candle项目和Nasbench-201搜索空间中的一组模型，我们算法性地分析和定量地表征模型演化的模式。我们展示了模型结构的演化如何受到正则化进化算法的影响，描述了演化模式在分布式环境中的出现以及缓存和改进调度的机会。最后，我们描述了影响特定模型架构在流动窗口中作为供体的频率，从而影响模型架构的流行程度的条件。

深度学习模型进化在网络架构搜索中的模式理解