The Simpler The Better: An Entropy-Based Importance Metric To Reduce Neural Networks' Depth

• URL: http://arxiv.org/abs/2404.18949v2
• Date: Wed, 5 Jun 2024 08:05:17 GMT
• Title: The Simpler The Better: An Entropy-Based Importance Metric To Reduce Neural Networks' Depth
• Authors: Victor Quétu, Zhu Liao, Enzo Tartaglione,
• Abstract summary: 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.
• Score: 5.869633234882029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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'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.

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