An In-Depth Analysis of Data Reduction Methods for Sustainable Deep Learning

arXiv:2403.15150v1 Announce Type: new
Abstract: In recent years, Deep Learning has gained popularity for its ability to solve complex classification tasks, increasingly delivering better results thanks to the development of more accurate models, the availability of huge volumes of data and the improved computational capabilities of modern computers. However, these improvements in performance also bring efficiency problems, related to the storage of datasets and models, and to the waste of energy and time involved in both the training and inference …

abstract analysis arxiv availability capabilities classification computational computers cs.cv cs.lg data data reduction deep learning development however modern results solve sustainable tasks type