Less Is More: The Influence of Pruning on the Explainability of CNNs Article Swipe
Florian Merkle
,
David J. Weber
,
Pascal Schöttle
,
Stephan Schlögl
,
Martin Nocker
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3569575
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3569575
Over the last century, deep learning models have become the state-of-the-art for solving complex computer vision problems. These modern computer vision models have millions of parameters, which presents two major challenges: 1) the increased computational requirements hamper the deployment in resource-constrained environments, such as mobile or IoT devices, and 2) explaining the complex decisions of such networks to humans is challenging. Network pruning is a technical approach to reduce the complexity of models, where less important parameters are removed. The work presented in this paper investigates whether this reduction in technical complexity also helps with perceived explainability. To do so, we conducted a pre-study and two human-grounded experiments, assessing the effects of different pruning ratios on explainability. Overall, we evaluate four different compression rates (i.e., 2, 4, 8, and 32) with 37 500 tasks on Mechanical Turk. Results indicate that lower compression rates have a positive influence on explainability, while higher compression rates show negative effects. Furthermore, we were able to identify sweet spots that increase both the perceived explainability and the model’s performance.
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Metadata
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- 10
- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4410343053Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/access.2025.3569575Digital Object Identifier
- Title
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Less Is More: The Influence of Pruning on the Explainability of CNNsWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
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Florian Merkle, David J. Weber, Pascal Schöttle, Stephan Schlögl, Martin NockerList of authors in order
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https://doi.org/10.1109/access.2025.3569575Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2025.3569575Direct OA link when available
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Pruning, Computer science, Artificial intelligence, Horticulture, BiologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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91Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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