DropNAS: Grouped Operation Dropout for Differentiable Architecture Search

TL;DR

DropNAS introduces a grouped operation dropout method to address co-adaptation and Matthew Effect issues in differentiable neural architecture search, leading to improved performance and robustness.

Contribution

The paper proposes DropNAS, a novel grouped operation dropout technique that fixes co-adaptation and Matthew Effect problems in DARTS-based NAS.

Findings

DropNAS achieves 2.26% test error on CIFAR-10.

DropNAS attains 16.39% on CIFAR-100.

DropNAS reaches 23.4% top-1 accuracy on ImageNet.

Abstract

Neural architecture search (NAS) has shown encouraging results in automating the architecture design. Recently, DARTS relaxes the search process with a differentiable formulation that leverages weight-sharing and SGD where all candidate operations are trained simultaneously. Our empirical results show that such procedure results in the co-adaption problem and Matthew Effect: operations with fewer parameters would be trained maturely earlier. This causes two problems: firstly, the operations with more parameters may never have the chance to express the desired function since those with less have already done the job; secondly, the system will punish those underperforming operations by lowering their architecture parameter, and they will get smaller loss gradients, which causes the Matthew Effect. In this paper, we systematically study these problems and propose a novel grouped operation dropout algorithm named DropNAS to fix the problems with DARTS. Extensive experiments demonstrate that DropNAS solves the above issues and achieves promising performance. Specifically, DropNAS achieves 2.26% test error on CIFAR-10, 16.39% on CIFAR-100 and 23.4% on ImageNet (with the same training hyperparameters as DARTS for a fair comparison). It is also observed that DropNAS is robust across variants of the DARTS search space. Code is available at https://github.com/wiljohnhong/DropNAS.