In this paper, we propose a neural architecture search framework based on a similarity measure between various tasks defined in terms of Fisher information. By utilizing the relation between a target and a set of existing tasks, the search space of architectures can be significantly reduced, making the discovery of the best candidates in the set of possible architectures tractable. This method eliminates the requirement for training the networks from scratch for the target task. Simulation results illustrate the efficacy of our proposed approach and its competitiveness with state-of-the-art methods.

翻译：在本文中,我们提议一个神经结构搜索框架,其依据是渔业信息方面界定的各种任务之间的类似度量;通过利用目标与一系列现有任务之间的关系,可以大大减少建筑的搜索空间,从而在一套可能的建筑中发现最佳候选人。这种方法消除了从头到尾对网络进行目标任务培训的要求。模拟结果显示了我们拟议方法的有效性及其与最新方法的竞争力。