Improving convolutional neural network design via variable neighborhood search
Improving convolutional neural network design via variable neighborhood search
dc.contributor.author | Teresa Finisterra Araújo | en |
dc.contributor.author | Guilherme Moreira Aresta | en |
dc.contributor.author | Bernardo Almada-Lobo | en |
dc.contributor.author | Ana Maria Mendonça | en |
dc.contributor.author | Aurélio Campilho | en |
dc.date.accessioned | 2018-01-14T17:05:02Z | |
dc.date.available | 2018-01-14T17:05:02Z | |
dc.date.issued | 2017 | en |
dc.description.abstract | An unsupervised method for convolutional neural network (CNN) architecture design is proposed. The method relies on a variable neighborhood search-based approach for finding CNN architectures and hyperparameter values that improve classification performance. For this purpose, t-Distributed Stochastic Neighbor Embedding (t-SNE) is applied to effectively represent the solution space in 2D. Then, k-Means clustering divides this representation space having in account the relative distance between neighbors. The algorithm is tested in the CIFAR-10 image dataset. The obtained solution improves the CNN validation loss by over 15% and the respective accuracy by 5%. Moreover, the network shows higher predictive power and robustness, validating our method for the optimization of CNN design. © Springer International Publishing AG 2017. | en |
dc.identifier.uri | http://repositorio.inesctec.pt/handle/123456789/6074 | |
dc.identifier.uri | http://dx.doi.org/10.1007/978-3-319-59876-5_41 | en |
dc.language | eng | en |
dc.relation | 6381 | en |
dc.relation | 6071 | en |
dc.relation | 5428 | en |
dc.relation | 6321 | en |
dc.relation | 6320 | en |
dc.rights | info:eu-repo/semantics/embargoedAccess | en |
dc.title | Improving convolutional neural network design via variable neighborhood search | en |
dc.type | conferenceObject | en |
dc.type | Publication | en |
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