<div>Machine learning models are rising every day. Most of the Computer Vision oriented</div><div>machine learning models arise from Convolutional Neural Network’s(CNN) basic structure.</div><div>Machine learning developers use CNNs extensively in Image classification, Object Recognition,</div><div>and Image segmentation. Although CNN produces highly compatible models with</div><div>superior accuracy, they have their disadvantages. Estimating pose and transformation for</div><div>computer vision applications is a difficult task for CNN. The CNN’s functions are capable of</div><div>learning only shift-invariant features of an image. These limitations give machine learning</div><div>developers motivation towards generating more complex algorithms.</div><div>Search for new machine learning models led to Capsule Networks. This Capsule Network</div><div>was able to estimate objects’ pose in an image and recognize transformations to these</div><div>objects. Handwritten digit classification is the task for which capsule networks are to solve</div><div>at the initial stages. Capsule Networks outperforms all models for the MNIST dataset for</div><div>handwritten digits, but to use Capsule networks for image classification is not a straightforward</div><div>multiplication of parameters. By replacing the Capsule Network’s initial layer, a</div><div>simple Convolutional Layer, with complex architectures in CNNs, authors of Residual Capsule</div><div>Network achieved a tremendous change in capsule network applications without a high</div><div>number of parameters.</div><div>This thesis focuses on improving this recent Residual Capsule Network (RCN) to an</div><div>extent where accuracy and model size is optimal for the Image classification task with a</div><div>benchmark of the CIFAR-10 dataset. Our search for an exemplary capsule network led to</div><div>the invention of RCN2: Residual Capsule Network 2 and RCNX: Residual Capsule NeXt.</div><div>RCNX, as the next generation of RCN. They outperform existing architectures in the domain</div><div>of Capsule networks, focusing on image classification such as 3-level RCN, DCNet, DC</div><div>Net++, Capsule Network, and even outperforms compact CNNs like MobileNet V3.</div><div>RCN2 achieved an accuracy of 85.12% with 1.95 Million parameters, and RCNX achieved</div><div>89.31% accuracy with 1.58 Million parameters on the CIFAR-10 benchmark.</div>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/14489091 |
| Date | 10 May 2021 |
| Creators | Arjun Narukkanchira Anilkumar (10702419) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/RCNX_RESIDUAL_CAPSULE_NEXT/14489091 |
Page generated in 0.0125 seconds