Synchronized Multi-Augmentation with Multi-Backbone Ensembling for Enhancing Deep Learning Performance

Abstract

This study introduces a novel technique called Synchronized Multi-Augmentation (SMA) combined with multi-backbone (MB) ensembling to enhance model performance and generalization in deep learning (DL) tasks in real-world scenarios. SMA utilizes synchronously augmented input data for training across multiple backbones, improving the overall feature extraction process. The outputs from these backbones are fused using two distinct strategies: the averaging fusion method, which averages predictions, and the dense fusion method, which averages features through a fully connected network. These methods aim to boost accuracy and reduce computational costs, particularly in Edge Intelligence (EI) systems with limited resources. The proposed SMA technique was evaluated on the CIFAR-10 dataset, highlighting its potential to enhance classification tasks in DL workflows. This study provides a comprehensive analysis of various backbones, their ensemble methods, and the impact of different SMAs on model performance. The results demonstrate that SMAs involving color adjustments, such as contrast and equalization, significantly improve generalization under varied lighting conditions that simulated real-world low-illumination conditions, outperforming traditional spatial augmentations. This approach is particularly beneficial for EI hardware, such as microcontrollers and IoT devices, which operate under strict constraints like limited processing power and memory and real-time processing requirements. This study’s findings suggest that employing SMA and MB ensembling can offer significant improvements in accuracy, generalization, and efficiency, making it a viable solution for deploying DL models on edge devices with constrained resources under real-world practical conditions.