Towards more efficient initialization methods for Convolutional Neural Networks via K-Means and Principal Components
This paper presents an exploration of unsupervised methods for initializing and training filters in convolutional layers, aiming to reduce the dependency on labeled data and computational resources. We propose two unsupervised methods based on the distribution of input data and evaluate their perfo...
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| Format: | Article |
| Language: | English |
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Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata
2025-04-01
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| Series: | Journal of Computer Science and Technology |
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| Online Access: | https://journal.info.unlp.edu.ar/JCST/article/view/3490 |
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| _version_ | 1850281043801669632 |
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| author | Federico Rabinovich Facundo Quiroga Franco Ronchetti |
| author_facet | Federico Rabinovich Facundo Quiroga Franco Ronchetti |
| author_sort | Federico Rabinovich |
| collection | DOAJ |
| description |
This paper presents an exploration of unsupervised methods for initializing and training filters in convolutional layers, aiming to reduce the dependency on labeled data and computational resources. We propose two unsupervised methods based on the distribution of input data and evaluate their performance against traditional Glorot Uniform initialization. By initializing solely the initial layer of a basic CNN network with one of our proposed methods, we attained a 0.78\% enhancement in final accuracy compared to traditional Glorot Uniform initialization. Our findings suggest that these unsupervised methods could serve as effective alternatives for filter initialization, potentially leading to more efficient training processes and a better understanding of CNNs.
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| format | Article |
| id | doaj-art-0b989766b58640418b00308484c68e5d |
| institution | OA Journals |
| issn | 1666-6046 1666-6038 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata |
| record_format | Article |
| series | Journal of Computer Science and Technology |
| spelling | doaj-art-0b989766b58640418b00308484c68e5d2025-08-20T01:48:29ZengPostgraduate Office, School of Computer Science, Universidad Nacional de La PlataJournal of Computer Science and Technology1666-60461666-60382025-04-0125110.24215/16666038.25.e04Towards more efficient initialization methods for Convolutional Neural Networks via K-Means and Principal ComponentsFederico Rabinovich0Facundo Quiroga1Franco Ronchetti2UNLPIII-LIDI, Facultad de Informática, UNLP - CICIII-LIDI, Facultad de Informática, UNLP - CIC This paper presents an exploration of unsupervised methods for initializing and training filters in convolutional layers, aiming to reduce the dependency on labeled data and computational resources. We propose two unsupervised methods based on the distribution of input data and evaluate their performance against traditional Glorot Uniform initialization. By initializing solely the initial layer of a basic CNN network with one of our proposed methods, we attained a 0.78\% enhancement in final accuracy compared to traditional Glorot Uniform initialization. Our findings suggest that these unsupervised methods could serve as effective alternatives for filter initialization, potentially leading to more efficient training processes and a better understanding of CNNs. https://journal.info.unlp.edu.ar/JCST/article/view/3490ClusteringCNN, Deep learning,K-Means ClusteringPrincipal Component Analysis |
| spellingShingle | Federico Rabinovich Facundo Quiroga Franco Ronchetti Towards more efficient initialization methods for Convolutional Neural Networks via K-Means and Principal Components Journal of Computer Science and Technology Clustering CNN, Deep learning, K-Means Clustering Principal Component Analysis |
| title | Towards more efficient initialization methods for Convolutional Neural Networks via K-Means and Principal Components |
| title_full | Towards more efficient initialization methods for Convolutional Neural Networks via K-Means and Principal Components |
| title_fullStr | Towards more efficient initialization methods for Convolutional Neural Networks via K-Means and Principal Components |
| title_full_unstemmed | Towards more efficient initialization methods for Convolutional Neural Networks via K-Means and Principal Components |
| title_short | Towards more efficient initialization methods for Convolutional Neural Networks via K-Means and Principal Components |
| title_sort | towards more efficient initialization methods for convolutional neural networks via k means and principal components |
| topic | Clustering CNN, Deep learning, K-Means Clustering Principal Component Analysis |
| url | https://journal.info.unlp.edu.ar/JCST/article/view/3490 |
| work_keys_str_mv | AT federicorabinovich towardsmoreefficientinitializationmethodsforconvolutionalneuralnetworksviakmeansandprincipalcomponents AT facundoquiroga towardsmoreefficientinitializationmethodsforconvolutionalneuralnetworksviakmeansandprincipalcomponents AT francoronchetti towardsmoreefficientinitializationmethodsforconvolutionalneuralnetworksviakmeansandprincipalcomponents |