# DC-NAS: Divide-and-Conquer Neural Architecture Search

arXiv: https://arxiv.org/abs/2005.14456 · HTML: https://mlsanity.com/paper/2005.14456/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2020.

## Built on by (most cited first, 1 of 1)

- [Stronger NAS with Weaker Predictors](https://mlsanity.com/paper/2102.10490/) (2021)

## Abstract (arXiv)

Most applications demand high-performance deep neural architectures costing limited resources. Neural architecture searching is a way of automatically exploring optimal deep neural networks in a given huge search space. However, all sub-networks are usually evaluated using the same criterion; that is, early stopping on a small proportion of the training dataset, which is an inaccurate and highly complex approach. In contrast to conventional methods, here we present a divide-and-conquer (DC) approach to effectively and efficiently search deep neural architectures. Given an arbitrary search space, we first extract feature representations of all sub-networks according to changes in parameters or output features of each layer, and then calculate the similarity between two different sampled networks based on the representations. Then, a k-means clustering is conducted to aggregate similar architectures into the same cluster, separately executing sub-network evaluation in each cluster. The best architecture in each cluster is later merged to obtain the optimal neural architecture. Experimental results conducted on several benchmarks illustrate that DC-NAS can overcome the inaccurate evaluation problem, achieving a 75.1% top-1 accuracy on the ImageNet dataset, which is higher than that of state-of-the-art methods using the same search space.