Neural Architecture Search (NAS) has shown excellent results in designing
architectures for computer vision problems. NAS alleviates the need for
human-defined settings by automating architecture design and engineering.
However, NAS methods tend to be slow, as they require large amounts of GPU
computation. This bottleneck is mainly due to the performance estimation
strategy, which requires the evaluation of the generated architectures, mainly
by training them, to update the sampler method. In this paper, we propose
EPE-NAS, an efficient performance estimation strategy, that mitigates the
problem of evaluating networks, by scoring untrained networks and creating a
correlation with their trained performance. We perform this process by looking
at intra and inter-class correlations of an untrained network. We show that
EPE-NAS can produce a robust correlation and that by incorporating it into a
simple random sampling strategy, we are able to search for competitive
networks, without requiring any training, in a matter of seconds using a single
GPU. Moreover, EPE-NAS is agnostic to the search method, since it focuses on
the evaluation of untrained networks, making it easy to integrate into almost
any NAS method.

本文提出了一种高效的性能评估策略 - EPE-NAS，通过评分未训练的神经网络并与其训练后的表现建立相关性，旨在解决神经架构搜索中耗时的性能估计问题，进而能够在几秒钟内使用单个 GPU 进行搜索。此方法仅需使用简单的随机取样策略，可支持几乎所有 NAS 方法的集成。

EPE-NAS: 用于神经架构搜索的高效性能估计方法

EPE-NAS: Efficient Performance Estimation Without Training for Neural  Architecture Search

To what extent is the success of deep visualization due to the training?
Could we do deep visualization using untrained, random weight networks? To
address this issue, we explore new and powerful generative models for three
popular deep visualization tasks using untrained, random weight convolutional
neural networks. First we invert representations in feature spaces and
reconstruct images from white noise inputs. The reconstruction quality is
statistically higher than that of the same method applied on well trained
networks with the same architecture. Next we synthesize textures using scaled
correlations of representations in multiple layers and our results are almost
indistinguishable with the original natural texture and the synthesized
textures based on the trained network. Third, by recasting the content of an
image in the style of various artworks, we create artistic images with high
perceptual quality, highly competitive to the prior work of Gatys et al. on
pretrained networks. To our knowledge this is the first demonstration of image
representations using untrained deep neural networks. Our work provides a new
and fascinating tool to study the representation of deep network architecture
and sheds light on new understandings on deep visualization.

使用未训练的卷积神经网络，创建新的生成模型，用于图像重建、纹理合成和样式转换等深度可视化任务，并发现了新的深度网络结构表示学习的一些特性和规律。