{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:52:47Z","timestamp":1773805967399,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"JSPS KAKENHI","award":["22H00548"],"award-info":[{"award-number":["22H00548"]}]},{"name":"JST CRONOS","award":["JPMJCS24K4"],"award-info":[{"award-number":["JPMJCS24K4"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Neural network pruning is essential for deploying deep learning models on resource-constrained devices by reducing computational and memory demands. In this paper, we propose a novel pruning framework, Entropy-Guided Search Space Optimization for Efficient Neural Network Pruning, which uses information entropy to assess the importance of convolutional layers. Specifically, we calculate the layer-wise entropy of pretrained weights, apply outlier detection to remove extreme values, and normalize the entropy values. These normalized values guide the selection of retention ratios, ensuring that layers with higher entropy retain more filters. By refining the subnetwork search space, our approach enhances the efficiency of the search process and improves overall subnetwork performance. The refined search space targets more promising regions, reducing computational overhead and resulting in higher-quality pruned networks. Through iterative optimization, the optimal subnetwork is identified and fine-tuned to produce the final pruned model. Experimental results on benchmark datasets show that our method significantly outperforms existing pruning methods, achieving substantial improvements in both accuracy and computational efficiency. This entropy-guided pruning strategy provides a robust and effective solution for neural network compression, suitable for a wide range of deep learning applications.<\/jats:p>","DOI":"10.3390\/a18120736","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T14:59:04Z","timestamp":1763996344000},"page":"736","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Entropy-Guided Search Space Optimization for Efficient Neural Network Pruning"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8841-8648","authenticated-orcid":false,"given":"Yicheng","family":"Qiu","sequence":"first","affiliation":[{"name":"Department of Informatics, The University of Electro-Communications, Chofu 182-8585, Tokyo, Japan"}]},{"given":"Li","family":"Niu","sequence":"additional","affiliation":[{"name":"Big Data Technology Research Center, Nanhu Laboratory, Jiaxing 314000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0005-3826","authenticated-orcid":false,"given":"Feng","family":"Sha","sequence":"additional","affiliation":[{"name":"Big Data Technology Research Center, Nanhu Laboratory, Jiaxing 314000, China"}]},{"given":"Zhaokun","family":"Cheng","sequence":"additional","affiliation":[{"name":"Big Data Technology Research Center, Nanhu Laboratory, Jiaxing 314000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0431-183X","authenticated-orcid":false,"given":"Keiji","family":"Yanai","sequence":"additional","affiliation":[{"name":"Department of Informatics, The University of Electro-Communications, Chofu 182-8585, Tokyo, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.Y. 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