Improving Convolutional Neural Network Design via Variable Neighborhood Search
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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.
KeywordsMachine learning Convolutional neural network Parameter optimization Variable neighborhood search
Teresa Araújo and Guilherme Aresta equally contributed to this work. Project “NanoSTIMA: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/NORTE-01-0145-FEDER-000016” is financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). Teresa Araújo is funded by the FCT grant contract SFRH/BD/122365/2016. Guilherme Aresta is funded by the FCT grant contract SFRH/BD/120435/2016.
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