Multi-path Neural Networks for On-device Multi-domain Visual Classification

TL;DR

This paper introduces a neural architecture search-based multi-path network for multi-domain visual classification on mobile devices, achieving state-of-the-art accuracy with significantly reduced parameters and FLOPS.

Contribution

It proposes an automated multi-path network design using reinforcement learning and adaptive domain prioritization, improving multi-domain learning efficiency and resource usage.

Findings

Achieves state-of-the-art accuracy on Visual Decathlon dataset.

Reduces parameters by 78% and FLOPS by 32% compared to single-domain models.

Improves average accuracy over individual single-domain models.

Abstract

Learning multiple domains/tasks with a single model is important for improving data efficiency and lowering inference cost for numerous vision tasks, especially on resource-constrained mobile devices. However, hand-crafting a multi-domain/task model can be both tedious and challenging. This paper proposes a novel approach to automatically learn a multi-path network for multi-domain visual classification on mobile devices. The proposed multi-path network is learned from neural architecture search by applying one reinforcement learning controller for each domain to select the best path in the super-network created from a MobileNetV3-like search space. An adaptive balanced domain prioritization algorithm is proposed to balance optimizing the joint model on multiple domains simultaneously. The determined multi-path model selectively shares parameters across domains in shared nodes while keeping domain-specific parameters within non-shared nodes in individual domain paths. This approach effectively reduces the total number of parameters and FLOPS, encouraging positive knowledge transfer while mitigating negative interference across domains. Extensive evaluations on the Visual Decathlon dataset demonstrate that the proposed multi-path model achieves state-of-the-art performance in terms of accuracy, model size, and FLOPS against other approaches using MobileNetV3-like architectures. Furthermore, the proposed method improves average accuracy over learning single-domain models individually, and reduces the total number of parameters and FLOPS by 78% and 32% respectively, compared to the approach that simply bundles single-domain models for multi-domain learning.