ProxylessNAS

2000년 도입 · 논문 17편에서 사용

ProxylessNAS directly learns neural network architectures on the target task and target hardware without any proxy task. Additional contributions include: - Using a new path-level pruning perspective for neural architecture search, showing a close connection between NAS and model compression. Memory consumption is saved by one order of magnitude by using path-level binarization. - Using a novel gradient-based approach (latency regularization loss) for handling hardware objectives (e.g. latency). Given different hardware platforms: CPU/GPU/Mobile, ProxylessNAS enables hardware-aware neural network specialization that’s exactly optimized for the target hardware.

출처: ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

소개 논문: ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware