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Improving Convolutional Neural Network Design via Variable Neighborhood Search
Authors
Ana Maria Mendonça
T Araujo
+3 more
G Aresta
Aurélio Campilho
Bernardo Almada Lobo
Publication date
1 January 2017
Publisher
'Springer Science and Business Media LLC'
Doi
Cite
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
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Last time updated on 10/07/2018
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