Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search

TL;DR

This paper introduces a path distillation approach for one-shot neural architecture search that improves subnetwork training and final architecture quality by collaboratively learning and selecting top-performing paths during training.

Contribution

The paper proposes a novel path distillation method using prioritized paths that enhances training convergence and architecture quality without complex search algorithms.

Findings

Improves convergence ratio and performance of hypernetworks.

Achieves superior accuracy compared to MobileNetV3 and EfficientNet.

Demonstrates robustness across object detection and diverse search spaces.

Abstract

One-shot weight sharing methods have recently drawn great attention in neural architecture search due to high efficiency and competitive performance. However, weight sharing across models has an inherent deficiency, i.e., insufficient training of subnetworks in hypernetworks. To alleviate this problem, we present a simple yet effective architecture distillation method. The central idea is that subnetworks can learn collaboratively and teach each other throughout the training process, aiming to boost the convergence of individual models. We introduce the concept of prioritized path, which refers to the architecture candidates exhibiting superior performance during training. Distilling knowledge from the prioritized paths is able to boost the training of subnetworks. Since the prioritized paths are changed on the fly depending on their performance and complexity, the final obtained paths are the cream of the crop. We directly select the most promising one from the prioritized paths as the final architecture, without using other complex search methods, such as reinforcement learning or evolution algorithms. The experiments on ImageNet verify such path distillation method can improve the convergence ratio and performance of the hypernetwork, as well as boosting the training of subnetworks. The discovered architectures achieve superior performance compared to the recent MobileNetV3 and EfficientNet families under aligned settings. Moreover, the experiments on object detection and more challenging search space show the generality and robustness of the proposed method. Code and models are available at https://github.com/microsoft/cream.git.