  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.

Machine Learning;Computer Vision and Pattern Recognition