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Capsule Networks (CapsNets) were proposed to mitigate the shortcomings of Convolutional Neural Networks (CNNs) such as invariance. Even though they have achieved equivariance, they fail to perform on the recognition of complex images and images with varied backgrounds such as CIFAR-10. Real-life images such as those found in plant disease datasets (aside from being complex with varied backgrounds) pose additional challenges such as class imbalance and the availability of a smaller number of annotated datasets. The original CapsNet uses CNNs as feature extractors, SoftMax for normalization, and dynamic routing (DR) to enable active capsules to make predictions resulting in the activation of higher-level capsules. However, CNNs do not serve as superior texture extractors and SoftMax restrains capsules from forming optimal coupling during routing. In this paper, we propose the use of an efficient texture descriptor (Local Binary Pattern -LBP), sigmoid function, and k-means routing respectively in place of CNN, SoftMax, and dynamic routing. We evaluate our model on six publicly available datasets; MNIST, fashion-MNIST, CIFAR-10, tomato, maize, and citrus datasets. Experimental results show that the proposed model generates fewer parameters and performs comparably well with the state-of-the-art multi-lane capsule networks on complex images. [ABSTRACT FROM AUTHOR]

Modern day computer vision tasks requires efficient solution to problems such as image recognition, natural language processing, object detection, object segmentation and language translation. Symbolic Artificial Intelligence with its hard coding rules is incapable of solving these complex problems resulting in the introduction of Deep Learning (DL) models such as Recurrent Neural Networks and Convolutional Neural Networks (CNN). However, CNNs require lots of training data and are incapable of recognizing pose and deformation of objects leading to the introduction of Capsule Networks. Capsule Networks are the new sensation in Deep Learning. They have lived to this expectation as their performance in relation to the above problems has been better than Convolutional Neural Networks. Even with this promise in performance, lack of architectural knowledge and inner workings of Capsules serves as a hindrance for researchers to take full advantage of this breakthrough. In this paper, we provide a comprehensive review of the state of the art architectures, tools and methodologies in existing implementations of capsule networks. We highlight the successes, failures and opportunities for further research to serve as a motivation to researchers and industry players to exploit the full potential of this new field. The main contribution of this survey article is that it explains and summarizes significant current state of the art Capsule Network architectures and implementations. [ABSTRACT FROM AUTHOR]