دانلود مقاله ISI انگلیسی شماره 132233
کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
132233 2017 22 صفحه PDF سفارش دهید 4682 کلمه
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عنوان انگلیسی
Fast DCNN based on FWT, intelligent dropout and layer skipping for image retrieval
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Neural Networks, Volume 95, November 2017, Pages 10-18

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چکیده انگلیسی

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.

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