@inproceedings{a5a176f843c441cca83f920c8ee373e2,
title = "The Simpler The Better: An Entropy-Based Importance Metric to Reduce Neural Networks{\textquoteright} Depth",
abstract = "While deep neural networks are highly effective at solving complex tasks, large pre-trained models are commonly employed even to solve consistently simpler downstream tasks, which do not necessarily require a large model{\textquoteright}s complexity. Motivated by the awareness of the ever-growing AI environmental impact, we propose an efficiency strategy that leverages prior knowledge transferred by large models. Simple but effective, we propose a method relying on an Entropy-bASed Importance mEtRic (EASIER) to reduce the depth of over-parametrized deep neural networks, which alleviates their computational burden. We assess the effectiveness of our method on traditional image classification setups. Our code is available at https://github.com/VGCQ/EASIER.",
keywords = "Compression, Deep Learning, Efficiency",
author = "Victor Qu{\'e}tu and Zhu Liao and Enzo Tartaglione",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024 ; Conference date: 09-09-2024 Through 13-09-2024",
year = "2024",
month = jan,
day = "1",
doi = "10.1007/978-3-031-70365-2\_6",
language = "English",
isbn = "9783031703645",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "92--108",
editor = "Albert Bifet and Jesse Davis and Tomas Krilavi{\v c}ius and Meelis Kull and Eirini Ntoutsi and Indrė {\v Z}liobaitė",
booktitle = "Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Proceedings",
}