MixPath: A Unified Approach for One-shot Neural Architecture Search

Xiangxiang Chu, Shun Lu, Xudong Li, Bo Zhang

Keywords: neural architecture search, multi-path, one-shot

TL;DR: A multi-path one-shot neural architecture search approach

Abstract: Blending multiple convolutional kernels is proved advantageous in neural architecture design. However, current two-stage neural architecture search methods are mainly limited to single-path search spaces. How to efficiently search models of multi-path structures remains a difficult problem. In this paper, we are motivated to train a one-shot multi-path supernet to accurately evaluate the candidate architectures. Specifically, we discover that in the popular search spaces, feature vectors summed from multiple paths are nearly multiples of those from a single path. Such disparity perturbs the supernet training and its ranking ability. Therefore, we propose a novel mechanism called \emph{Shadow Batch Normalization} (SBN) to regularize the disparate feature statistics. Extensive experiments prove that SBNs are capable of stabilizing the optimization and improving ranking performance by a clear margin. We call our unified multi-path one-shot approach as MixPath, which efficiently generates a series of competitive models on ImageNet.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Submission Guidelines: Yes

Please Choose The Closest Area That Your Submission Falls Into: Deep Learning and representational learning

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/mixpath-a-unified-approach-for-one-shot/code)