Please use this identifier to cite or link to this item:
https://hdl.handle.net/10216/112010
Author(s): | Araujo, T Aresta, G Bernardo Almada Lobo Ana Maria Mendonça Aurélio Campilho |
Title: | Improving Convolutional Neural Network Design via Variable Neighborhood Search |
Issue Date: | 2017 |
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. (c) Springer International Publishing AG 2017. |
URI: | https://hdl.handle.net/10216/112010 |
Source: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Document Type: | Artigo em Livro de Atas de Conferência Internacional |
Rights: | openAccess |
Appears in Collections: | FEUP - Artigo em Livro de Atas de Conferência Internacional |
Files in This Item:
File | Description | Size | Format | |
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223497.pdf | submissão | 1.3 MB | Adobe PDF | View/Open |
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