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In recent years, convolutional neural networks (CNN) have played an importantrole in the field of deep learning. Variants of CNN's have proven to be verysuccessful in classification tasks across different domains. However, there aretwo big drawbacks to CNN's: their failure to take into account of importantspatial hierarchies between features, and their lack of rotational invariance.As long as certain key features of an object are present in the test data,CNN's classify the test data as the object, disregarding features' relativespatial orientation to each other. This causes false positives. The lack ofrotational invariance in CNN's would cause the network to incorrectly assignthe object another label, causing false negatives. To address this concern,Hinton et al. propose a novel type of neural network using the concept ofcapsules in a recent paper. With the use of dynamic routing and reconstructionregularization, the capsule network model would be both rotation invariant andspatially aware. The capsule network has shown its potential by achieving astate-of-the-art result of 0.25% test error on MNIST without data augmentationsuch as rotation and scaling, better than the previous baseline of 0.39%. Tofurther test out the application of capsule networks on data with higherdimensionality, we attempt to find the best set of configurations that yieldthe optimal test error on CIFAR10 dataset.

Capsule Network Performance on Complex Data