Neural Architecture Search From Fréchet Task Distance
ArXiv(2021)
摘要
We formulate a Fréchet-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 other methods.
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关键词
fréchet task distance,architecture,search
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