MobileNet-HeX: Heterogeneous Ensemble of MobileNet eXperts for Efficient and Scalable Vision Model Optimization
Year: 2024
Type: article
Abstract: Efficient and accurate vision models are essential for real-world applications such as medical imaging and deepfake detection, where both performance and computational efficiency are critical. While recent vision models achieve high accuracy, they often come with the trade-off of increased size and computational demands. In this work, we propose MobileNet-HeX, a new ensemble model based on Heterogeneous MobileNet eXperts, designed to achieve top-tier performance while minimizing computational demands in real-world vision tasks. By utilizing a two-step Expand-and-Squeeze mechanism, MobileNet-HeX first expands a MobileNet population through diverse random training setups. It then squeezes the population through pruning, selecting the top-performing models based on heterogeneity and validation performance metrics. Finally, the selected Heterogeneous eXpert MobileNets are combined via sequential quadratic programming to form an efficient super-learner. MobileNet-HeX is benchmarked against state-of-the-art vision models in challenging case studies, such as skin cancer classification and deepfake detection. The results demonstrate that MobileNet-HeX not only surpasses these models in performance but also excels in speed and memory efficiency. By effectively leveraging a diverse set of MobileNet eXperts, we experimentally show that small, yet highly optimized, models can outperform even the most powerful vision networks in both accuracy and computational efficiency.
Source: Big Data and Cognitive Computing
DOI: 10.3390/bdcc9010002
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,046
OpenAlex ID: W4405953318