Enhancing Adaptive Deep Networks for Image Classification via Uncertainty-aware Decision Fusion

TL;DR

This paper introduces a novel uncertainty-aware decision fusion module, CDM, that combines multiple classifier heads in adaptive deep networks to improve image classification accuracy under varying computational resources.

Contribution

The paper proposes the CDM module with evidential deep learning-based fusion and a guided training strategy, enhancing adaptive network performance over existing methods.

Findings

Achieves 0.4% to 2.8% accuracy improvements on ImageNet.

Effectively fuses multiple classifiers for better inference.

Demonstrates robustness across different adaptive network architectures.

Abstract

Handling varying computational resources is a critical issue in modern AI applications. Adaptive deep networks, featuring the dynamic employment of multiple classifier heads among different layers, have been proposed to address classification tasks under varying computing resources. Existing approaches typically utilize the last classifier supported by the available resources for inference, as they believe that the last classifier always performs better across all classes. However, our findings indicate that earlier classifier heads can outperform the last head for certain classes. Based on this observation, we introduce the Collaborative Decision Making (CDM) module, which fuses the multiple classifier heads to enhance the inference performance of adaptive deep networks. CDM incorporates an uncertainty-aware fusion method based on evidential deep learning (EDL), that utilizes the reliability (uncertainty values) from the first c-1 classifiers to improve the c-th classifier' accuracy. We also design a balance term that reduces fusion saturation and unfairness issues caused by EDL constraints to improve the fusion quality of CDM. Finally, a regularized training strategy that uses the last classifier to guide the learning process of early classifiers is proposed to further enhance the CDM module's effect, called the Guided Collaborative Decision Making (GCDM) framework. The experimental evaluation demonstrates the effectiveness of our approaches. Results on ImageNet datasets show CDM and GCDM obtain 0.4% to 2.8% accuracy improvement (under varying computing resources) on popular adaptive networks. The code is available at the link https://github.com/Meteor-Stars/GCDM_AdaptiveNet.