AutoSlim: Towards One-Shot Architecture Search for Channel Numbers

TL;DR

AutoSlim introduces a one-shot method to optimize neural network channel configurations by training a single slimmable network, leading to improved accuracy and resource efficiency across various models and constraints.

Contribution

The paper proposes AutoSlim, a novel one-shot approach for channel number optimization that outperforms existing methods in accuracy and resource utilization without extensive search.

Findings

AutoSlim achieves higher accuracy than default configurations.

It outperforms recent channel pruning and neural architecture search methods.

Significant accuracy improvements on ImageNet across multiple models.

Abstract

We study how to set channel numbers in a neural network to achieve better accuracy under constrained resources (e.g., FLOPs, latency, memory footprint or model size). A simple and one-shot solution, named AutoSlim, is presented. Instead of training many network samples and searching with reinforcement learning, we train a single slimmable network to approximate the network accuracy of different channel configurations. We then iteratively evaluate the trained slimmable model and greedily slim the layer with minimal accuracy drop. By this single pass, we can obtain the optimized channel configurations under different resource constraints. We present experiments with MobileNet v1, MobileNet v2, ResNet-50 and RL-searched MNasNet on ImageNet classification. We show significant improvements over their default channel configurations. We also achieve better accuracy than recent channel pruning methods and neural architecture search methods. Notably, by setting optimized channel numbers, our AutoSlim-MobileNet-v2 at 305M FLOPs achieves 74.2% top-1 accuracy, 2.4% better than default MobileNet-v2 (301M FLOPs), and even 0.2% better than RL-searched MNasNet (317M FLOPs). Our AutoSlim-ResNet-50 at 570M FLOPs, without depthwise convolutions, achieves 1.3% better accuracy than MobileNet-v1 (569M FLOPs). Code and models will be available at: https://github.com/JiahuiYu/slimmable_networks