Effects of Degradations on Deep Neural Network Architectures
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Deep convolutional neural networks (CNNs) have achieved many state-of-the-art results recently in different fields of computer vision. Different architectures are emerging almost everyday that produce fascinating results in machine vision tasks consisting million of images. Though CNN based models are able to detect and recognize large number of object classes, most of the times these models are trained with high quality images. Recently, an alternative architecture for image classification is proposed by Sabour et al. based on groups of neurons (capsules) and a novel dynamic routing protocol. The architecture has shown promising result by surpassing the performances of state-of-the-art CNN based models in some of the datasets. However, the behavior of capsule based models and CNN based models are largely unknown in presence of noise. As in most of the practical applications, it can not be guaranteed the input image is completely noise-free and does not contain any distortion, it is important to study the performance of these models under different noise conditions. In this paper, we select six widely used CNN architectures and consider their performances for image classification task for two different dataset under image quality distortions. We show that the capsule network is more robust to several image degradations than the CNN based models, especially for salt and pepper noise. To the best of our knowledge, this is the first study on the performance of CapsuleNet and other state-of-the-art CNN architectures under different types of image degradations.
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