Multi-path learnable wavelet neural network for image classification

D. D. N. De Silva; H. W. M. K. Vithanage; K. S. D. Fernando; I. T. S. Piyatilake

doi:10.1117/12.2556535

31 January 2020 Multi-path learnable wavelet neural network for image classification

D. D. N. De Silva, H. W. M. K. Vithanage, K. S. D. Fernando, I. T. S. Piyatilake

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114331O (2020) https://doi.org/10.1117/12.2556535
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

Despite the remarkable success of deep learning in pattern recognition, deep network models face the problem of training a large number of parameters. In this paper, we propose and evaluate a novel multi-path wavelet neural network architecture for image classification with far less number of trainable parameters. The model architecture consists of a multi-path layout with several levels of wavelet decompositions performed in parallel followed by fully connected layers. These decomposition operations comprise wavelet neurons with learnable parameters, which are updated during the training phase using the back-propagation algorithm. We evaluate the performance of the introduced network using common image datasets without data augmentation except for SVHN and compare the results with influential deep learning models. Our findings support the possibility of reducing the number of parameters significantly in deep neural networks without compromising its accuracy.

Citation Download Citation

D. D. N. De Silva, H. W. M. K. Vithanage, K. S. D. Fernando, and I. T. S. Piyatilake "Multi-path learnable wavelet neural network for image classification", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114331O (31 January 2020); https://doi.org/10.1117/12.2556535

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