RARTS: An Efficient First-Order Relaxed Architecture Search Method

TL;DR

RARTS introduces a single-level, relaxed architecture search method that improves accuracy and efficiency over DARTS by utilizing the entire dataset and network cooperation without second derivatives.

Contribution

The paper proposes RARTS, a novel architecture search algorithm that simplifies and accelerates differentiable NAS while maintaining or improving accuracy, without involving second derivatives.

Findings

RARTS achieves 60% reduction in computational cost compared to second-order DARTS on CIFAR-10.

RARTS outperforms DARTS in accuracy on CIFAR-10 and ImageNet.

RARTS surpasses traditional pruning benchmarks in width search tasks.

Abstract

Differentiable architecture search (DARTS) is an effective method for data-driven neural network design based on solving a bilevel optimization problem. Despite its success in many architecture search tasks, there are still some concerns about the accuracy of first-order DARTS and the efficiency of the second-order DARTS. In this paper, we formulate a single level alternative and a relaxed architecture search (RARTS) method that utilizes the whole dataset in architecture learning via both data and network splitting, without involving mixed second derivatives of the corresponding loss functions like DARTS. In our formulation of network splitting, two networks with different but related weights cooperate in search of a shared architecture. The advantage of RARTS over DARTS is justified by a convergence theorem and an analytically solvable model. Moreover, RARTS outperforms DARTS and its variants in accuracy and search efficiency, as shown in adequate experimental results. For the task of searching topological architecture, i.e., the edges and the operations, RARTS obtains a higher accuracy and 60\% reduction of computational cost than second-order DARTS on CIFAR-10. RARTS continues to out-perform DARTS upon transfer to ImageNet and is on par with recent variants of DARTS even though our innovation is purely on the training algorithm without modifying search space. For the task of searching width, i.e., the number of channels in convolutional layers, RARTS also outperforms the traditional network pruning benchmarks. Further experiments on the public architecture search benchmark like NATS-Bench also support the preeminence of RARTS.