Operation-level Progressive Differentiable Architecture Search

TL;DR

This paper introduces OPP-DARTS, a new NAS method that progressively increases candidate operations during search to improve stability, performance, and robustness over standard DARTS, especially by mitigating skip connection issues.

Contribution

OPP-DARTS proposes a staged search process with progressive operation addition to enhance DARTS stability and explore better architectures, addressing skip connection aggregation problems.

Findings

Outperforms standard DARTS on CIFAR-10 in accuracy.

Demonstrates improved robustness across multiple search spaces.

Shows effective mitigation of skip connection issues.

Abstract

Differentiable Neural Architecture Search (DARTS) is becoming more and more popular among Neural Architecture Search (NAS) methods because of its high search efficiency and low compute cost. However, the stability of DARTS is very inferior, especially skip connections aggregation that leads to performance collapse. Though existing methods leverage Hessian eigenvalues to alleviate skip connections aggregation, they make DARTS unable to explore architectures with better performance. In the paper, we propose operation-level progressive differentiable neural architecture search (OPP-DARTS) to avoid skip connections aggregation and explore better architectures simultaneously. We first divide the search process into several stages during the search phase and increase candidate operations into the search space progressively at the beginning of each stage. It can effectively alleviate the unfair competition between operations during the search phase of DARTS by offsetting the inherent unfair advantage of the skip connection over other operations. Besides, to keep the competition between operations relatively fair and select the operation from the candidate operations set that makes training loss of the supernet largest. The experiment results indicate that our method is effective and efficient. Our method's performance on CIFAR-10 is superior to the architecture found by standard DARTS, and the transferability of our method also surpasses standard DARTS. We further demonstrate the robustness of our method on three simple search spaces, i.e., S2, S3, S4, and the results show us that our method is more robust than standard DARTS. Our code is available at https://github.com/zxunyu/OPP-DARTS.