Capsule networks for low-data transfer learning

Andrew Gritsevskiy; Maksym Korablyov

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arXiv · 1804.10172

Capsule networks for low-data transfer learning

Andrew Gritsevskiy·Maksym Korablyov

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Abstract

We propose a capsule network-based architecture for generalizing learning to new data with few examples. Using both generative and non-generative capsule networks with intermediate routing, we are able to generalize to new information over 25 times faster than a similar convolutional neural network. We train the networks on the multiMNIST dataset lacking one digit. After the networks reach their maximum accuracy, we inject 1-100 examples of the missing digit into the training set, and measure the number of batches needed to return to a comparable level of accuracy. We then discuss the improvement in low-data transfer learning that capsule networks bring, and propose future directions for capsule research.

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BibTeX RIS

Andrew Gritsevskiy, Maksym Korablyov. 2018-04-26. Capsule networks for low-data transfer learning. https://arxiv.org/abs/1804.10172

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