2021/05/27 by Emanuele Vitali, Vitali, Emanuele, Anton Lokhmotov +3
Computer Science · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #Computer Vision and Pattern Recognition (cs.CV) #Distributed #FOS: Computer and information sciences #Machine Learning and Data Classification #Parallel #and Cluster Computing (cs.DC) #cs.CV #cs.DC
paper · pdf · doi:10.48550/arxiv.2105.13279
Paper accepted at SAMOS21 - International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation
arxiv created 2021/05/27 · openalex publication_date 2021/05/27 · arxiv updated 2021/05/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we want to show the potential benefit of a dynamic auto-tuning approach for the inference process in the Deep Neural Network (DNN) context, tackling the object detection challenge. We benchmarked different neural networks to find the optimal detector for the well-known COCO 17 database, and we demonstrate that even if we only consider the quality of the prediction there is not a single optimal network. This is even more evident if we also consider the time to solution as a metric to evaluate, and then select, the most suitable network. This opens to the possibility for an adaptive methodology to switch among different object detection networks according to run-time requirements (e.g. maximum quality subject to a time-to-solution constraint). Moreover, we demonstrated by developing an ad hoc oracle, that an additional proactive methodology could provide even greater benefits, allowing us to select the best network among the available ones given some characteristics of the processed image. To exploit this method, we need to identify some image features that can be used to steer the decision on the most promising network. Despite the optimization opportunity that has been identified, we were not able to identify a predictor function that validates this attempt neither adopting classical image features nor by using a DNN classifier.