Fast DCNN based on FWT, intelligent dropout and layer skipping for image retrieval. (November 2017)

• Record Type: Journal Article
• Authors: ElAdel, Asma; Zaied, Mourad; Amar, Chokri Ben
• Is Part Of: Neural networks. Volume 95(2017)
• Page Start: 10
• Page End: 18
• Publication Date: 2017-11
• Subjects: Deep Convolution Neural Network -- Intelligent dropout -- Fast Beta wavelet transform -- Image classification & retrieval; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2017.07.015
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Deep Convolutional Neural Network (DCNN) can be marked as a powerful tool for object and image classification and retrieval. However, the training stage of such networks is highly consuming in terms of storage space and time. Also, the optimization is still a challenging subject. In this paper, we propose a fast DCNN based on Fast Wavelet Transform (FWT), intelligent dropout and layer skipping. The proposed approach led to improve the image retrieval accuracy as well as the searching time. This was possible thanks to three key advantages: First, the rapid way to compute the features using FWT. Second, the proposed intelligent dropout method is based on whether or not a unit is efficiently and not randomly selected. Third, it is possible to classify the image using efficient units of earlier layer(s) and skipping all the subsequent hidden layers directly to the output layer. Our experiments were performed on CIFAR-10 and MNIST datasets and the obtained results are very promising.