Evaluating the Energy Efficiency of Few-Shot Learning for Object Detection in Industrial Settings

arXiv:2403.06631v1 Announce Type: new
Abstract: In the ever-evolving era of Artificial Intelligence (AI), model performance has constituted a key metric driving innovation, leading to an exponential growth in model size and complexity. However, sustainability and energy efficiency have been critical requirements during deployment in contemporary industrial settings, necessitating the use of data-efficient approaches such as few-shot learning. In this paper, to alleviate the burden of lengthy model training and minimize energy consumption, a finetuning approach to adapt standard object detection …

abstract artificial artificial intelligence arxiv complexity cs.ai cs.cv cs.lg deployment detection driving efficiency energy energy efficiency few-shot few-shot learning growth however industrial innovation intelligence key object performance requirements sustainability type