Residual Capsule Network

dc.contributor.advisorEl-Sharkawy, Mohamed
dc.contributor.authorBhamidi, Sree Bala Shruthi
dc.contributor.otherKing, Brian
dc.contributor.otherRizkalla, Maher
dc.date.accessioned2019-07-18T15:01:30Z
dc.date.available2019-07-18T15:01:30Z
dc.date.issued2019-08
dc.degree.date2019en_US
dc.degree.disciplineElectrical & Computer Engineeringen
dc.degree.grantorPurdue Universityen_US
dc.degree.levelM.S.en_US
dc.descriptionIndiana University-Purdue University Indianapolis (IUPUI)en_US
dc.description.abstractThe Convolutional Neural Network (CNN) have shown a substantial improvement in the field of Machine Learning. But they do come with their own set of drawbacks. Capsule Networks have addressed the limitations of CNNs and have shown a great improvement by calculating the pose and transformation of the image. Deeper networks are more powerful than shallow networks but at the same time, more difficult to train. Residual Networks ease the training and have shown evidence that they can give good accuracy with considerable depth. Putting the best of Capsule Network and Residual Network together, we present Residual Capsule Network and 3-Level Residual Capsule Network, a framework that uses the best of Residual Networks and Capsule Networks. The conventional Convolutional layer in Capsule Network is replaced by skip connections like the Residual Networks to decrease the complexity of the Baseline Capsule Network and seven ensemble Capsule Network. We trained our models on MNIST and CIFAR-10 datasets and have seen a significant decrease in the number of parameters when compared to the Baseline models.en_US
dc.identifier.urihttps://hdl.handle.net/1805/19902
dc.identifier.urihttp://dx.doi.org/10.7912/C2/2554
dc.language.isoenen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subjectResidual Networken_US
dc.subjectCapsule Networken_US
dc.subjectDynamic Routingen_US
dc.subjectImage Classificationen_US
dc.subjectNeural Networken_US
dc.titleResidual Capsule Networken_US
dc.typeThesisen
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