Fast deep neural network based on intelligent dropout and layer skipping
Asma ElAdel, Ridha Ejbali, Mourad Zaied, Chokri Ben Amar · 2017
Deep Convolutional Neural Network (DCNN) can be marked as a powerful tool for object and image classification. 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 smart dropout and layer skipping. The proposed approach led to improve the speed of the testing stage as well as image classification accuracy. This was possible thanks to three key advantages: First, the rapid way to compute the features using Fast Beta Wavelet Transform. 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.