We formulate a Fr\'echet-type asymmetric distance between tasks based on Fisher Information Matrices. We show how the distance between a target task and each task in a given set of baseline tasks can be used to reduce the neural architecture search space for the target task. The complexity reduction in search space for task-specific architectures is achieved by building on the optimized architectures for similar tasks instead of doing a full search without using this side information. Experimental results demonstrate the efficacy of the proposed approach and its improvements over the state-of-the-art methods.

翻译：我们根据渔业信息矩阵设计了Fr\'echet型任务之间不对称的距离。 我们展示了如何利用目标任务与特定一组基线任务中每项任务之间的距离来减少目标任务神经结构搜索空间。 任务特定结构搜索空间的复杂程度的减少是通过建立用于类似任务的优化结构来实现的,而不是在不使用这一侧信息的情况下进行全面搜索。 实验结果显示了拟议方法的有效性及其对最新方法的改进。