If NAS methods are solutions, what is the problem? Most existing NAS methods
require two-stage parameter optimization. However, performance of the same
architecture in the two stages correlates poorly. In this work, we propose a
new problem definition for NAS, task-specific end-to-end, based on this
observation. We argue that given a computer vision task for which a NAS method
is expected, this definition can reduce the vaguely-defined NAS evaluation to
i) accuracy of this task and ii) the total computation consumed to finally
obtain a model with satisfying accuracy. Seeing that most existing methods do
not solve this problem directly, we propose DSNAS, an efficient differentiable
NAS framework that simultaneously optimizes architecture and parameters with a
low-biased Monte Carlo estimate. Child networks derived from DSNAS can be
deployed directly without parameter retraining. Comparing with two-stage
methods, DSNAS successfully discovers networks with comparable accuracy (74.4%)
on ImageNet in 420 GPU hours, reducing the total time by more than 34%. Our
implementation is available at this https URL

通过提出基于任务的端到端的 NAS 方法定义，我们提出了 DSNAS，一个高效的可微分 NAS 框架，不仅可以同时优化架构和参数，而且还可以减少模型训练时间，并在 ImageNet 数据集上达到了与现有方法同等的准确率。

DSNAS: 无需参数微调的直接神经结构搜索

DSNAS: Direct Neural Architecture Search without Parameter Retraining

Finding a well-performing architecture is often tedious for both DL
practitioners and researchers, leading to tremendous interest in the automation
of this task by means of neural architecture search (NAS). Although the
community has made major strides in developing better NAS methods, the quality
of scientific empirical evaluations in the young field of NAS is still lacking
behind that of other areas of machine learning. To address this issue, we
describe a set of possible issues and ways to avoid them, leading to the NAS
best practices checklist available at this http URL

介绍了一系列可能出现的问题和避免它们的方法，从而制定出一份最佳实践清单，以提高神经结构搜索的科学实证评估水平。