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Dynamic Image Difficulty-Aware DNN Pruning
Deep Neural Networks (DNNs) have achieved impressive performance in various image recognition tasks, but their large model sizes make them challenging to deploy on resource-constrained devices. In this paper, we propose a dynamic DNN pruning approach that takes into account the difficulty of the inc...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10224338/ https://www.ncbi.nlm.nih.gov/pubmed/37241531 http://dx.doi.org/10.3390/mi14050908 |
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author | Pentsos, Vasileios Spantidi, Ourania Anagnostopoulos, Iraklis |
author_facet | Pentsos, Vasileios Spantidi, Ourania Anagnostopoulos, Iraklis |
author_sort | Pentsos, Vasileios |
collection | PubMed |
description | Deep Neural Networks (DNNs) have achieved impressive performance in various image recognition tasks, but their large model sizes make them challenging to deploy on resource-constrained devices. In this paper, we propose a dynamic DNN pruning approach that takes into account the difficulty of the incoming images during inference. To evaluate the effectiveness of our method, we conducted experiments on the ImageNet dataset on several state-of-art DNNs. Our results show that the proposed approach reduces the model size and amount of DNN operations without the need to retrain or fine-tune the pruned model. Overall, our method provides a promising direction for designing efficient frameworks for lightweight DNN models that can adapt to the varying complexity of input images. |
format | Online Article Text |
id | pubmed-10224338 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102243382023-05-28 Dynamic Image Difficulty-Aware DNN Pruning Pentsos, Vasileios Spantidi, Ourania Anagnostopoulos, Iraklis Micromachines (Basel) Article Deep Neural Networks (DNNs) have achieved impressive performance in various image recognition tasks, but their large model sizes make them challenging to deploy on resource-constrained devices. In this paper, we propose a dynamic DNN pruning approach that takes into account the difficulty of the incoming images during inference. To evaluate the effectiveness of our method, we conducted experiments on the ImageNet dataset on several state-of-art DNNs. Our results show that the proposed approach reduces the model size and amount of DNN operations without the need to retrain or fine-tune the pruned model. Overall, our method provides a promising direction for designing efficient frameworks for lightweight DNN models that can adapt to the varying complexity of input images. MDPI 2023-04-23 /pmc/articles/PMC10224338/ /pubmed/37241531 http://dx.doi.org/10.3390/mi14050908 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pentsos, Vasileios Spantidi, Ourania Anagnostopoulos, Iraklis Dynamic Image Difficulty-Aware DNN Pruning |
title | Dynamic Image Difficulty-Aware DNN Pruning |
title_full | Dynamic Image Difficulty-Aware DNN Pruning |
title_fullStr | Dynamic Image Difficulty-Aware DNN Pruning |
title_full_unstemmed | Dynamic Image Difficulty-Aware DNN Pruning |
title_short | Dynamic Image Difficulty-Aware DNN Pruning |
title_sort | dynamic image difficulty-aware dnn pruning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10224338/ https://www.ncbi.nlm.nih.gov/pubmed/37241531 http://dx.doi.org/10.3390/mi14050908 |
work_keys_str_mv | AT pentsosvasileios dynamicimagedifficultyawarednnpruning AT spantidiourania dynamicimagedifficultyawarednnpruning AT anagnostopoulosiraklis dynamicimagedifficultyawarednnpruning |