Conventional Neural Architecture Search (NAS) aims at finding a single
architecture that achieves the best performance, which usually optimizes task
related learning objectives such as accuracy. However, a single architecture
may not be representative enough for the whole dataset with high diversity and
variety. Intuitively, electing domain-expert architectures that are proficient
in domain-specific features can further benefit architecture related objectives
such as latency. In this paper, we propose InstaNAS---an instance-aware NAS
framework---that employs a controller trained to search for a "distribution of
architectures" instead of a single architecture; This allows the model to use
sophisticated architectures for the difficult samples, which usually comes with
large architecture related cost, and shallow architectures for those easy
samples. During the inference phase, the controller assigns each of the unseen
input samples with a domain expert architecture that can achieve high accuracy
with customized inference costs. Experiments within a search space inspired by
MobileNetV2 show InstaNAS can achieve up to 48.8% latency reduction without
compromising accuracy on a series of datasets against MobileNetV2.

本文提出了一种基于实例的神经架构搜索方法 InstaNAS，通过搜索一种 “架构分布” 而不是单一的神经架构，确保难以处理的样本使用复杂的神经架构来减少前处理时间，并在搜索空间中验证表明，与 MobileNetV2 相比，在多个数据集上具有相同的准确性和大约 48.8％的前处理时间减少。