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arXiv · 2411.14345

Layer Pruning with Consensus: A Triple-Win Solution

Leandro Giusti Mugnaini·Carolina Tavares Duarte·Anna H. Reali Costa·Artur Jordao
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Abstract

Layer pruning offers a promising alternative to standard structured pruning, effectively reducing computational costs, latency, and memory footprint. While notable layer-pruning approaches aim to detect unimportant layers for removal, they often rely on single criteria that may not fully capture the complex, underlying properties of layers. We propose a novel approach that combines multiple similarity metrics into a single expressive measure of low-importance layers, called the Consensus criterion. Our technique delivers a triple-win solution: low accuracy drop, high-performance improvement, and increased robustness to adversarial attacks. With up to 78.80% FLOPs reduction and performance on par with state-of-the-art methods across different benchmarks, our approach reduces energy consumption and carbon emissions by up to 66.99% and 68.75%, respectively. Additionally, it avoids shortcut learning and improves robustness by up to 4 percentage points under various adversarial attacks. Overall, the Consensus criterion demonstrates its effectiveness in creating robust, efficient, and environmentally friendly pruned models.

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Leandro Giusti Mugnaini, Carolina Tavares Duarte, Anna H. Reali Costa, Artur Jordao. 2024-11-21. Layer Pruning with Consensus: A Triple-Win Solution. https://doi.org/10.1109/access.2025.3601042

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