Capsule Neural Networks (CapsNets) have recently emerged as a new neural network architecture aimed at improving on the weaknesses of Convolutional Neural Networks (CNNs). CapsNets use “capsules” to learn advanced structures of features in objects and possess equivariance. They have already shown that they can perform better on classification tasks than state-of-the-art CNNs. Utilizing these strengths, we can apply CapsNets to other tasks other than classification such as image generation. Image generation is already possible from the masked output of high level capsules in a supervised model, but in this paper I propose a method which uses CapsNets in a Generative Adversarial Network (GAN) architecture to generate images in an unsupervised learning setting. These Unsupervised Generative CapsNets (UGCNs) outperform similarly sized Fully-Connected Dense layers in terms of image reconstruction quality and Fréchet Inception Distance on the MNIST and CIFAR-10 data sets.
|School:||Stevens Institute of Technology|
|School Location:||United States -- New Jersey|
|Source:||MAI 58/04M(E), Masters Abstracts International|
|Subjects:||Computer Engineering, Artificial intelligence, Computer science|
|Keywords:||Capsule neural networks, Deep learning, Generative adversarial networks, Image generation, Unsupervised capsule networks, Unsupervised learning|
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