<p>The 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.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/8947016 |
| Date | 13 August 2019 |
| Creators | Sree Bala Shrut Bhamidi (6990443) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Residual_Capsule_Network/8947016 |
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