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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
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 |
| Subjects: | |
| Online Access: | https://journal.info.unlp.edu.ar/JCST/article/view/3490 |
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