Tackling the small data problem in deep learning with multi-sensor approaches

Kramer, Iris, Caroline, Hare, Jonathon and Prugel-Bennett, Adam (2017) Tackling the small data problem in deep learning with multi-sensor approaches. 46th Computer Applications and Quantitative Methods in Archaeology: Human History and Digital Future, , Tübingen, Germany. 19 - 23 Mar 2018. (In Press)

Abstract

Within data science, many problems are solved using machine learning. Recently, with the introduction of deep learning, we see this trend spread out across industries of which archaeological object detection on remote sensor data is a case in point. From the known case studies, we have identified the main issues and developed improvements accordingly.

The main issue of archaeological datasets is that there are only a limited number of known sites which makes the networks prone to overfit. Overfitting happens when a network is trained on too few examples and learns patterns that do not generalize well to new data. To an extent, data augmentation can be used to prevent overfitting, however, the training images would still be highly correlated. Therefore, it is argued that the most effect can be gained by limiting storage of irrelevant features in networks. This can be done by optimising network architectures and additionally by using transfer learning in which pre-trained network are used to initialise training. Regardless of pre-training on datasets without archaeological sites, its trained network can still be useful for the low-level features (including lines and edges). A downside of pre-trained networks is that they can only work with data in the same format as they had been trained with.

Our main contribution is the research into including multi-sensor data. We will present approaches to train networks using images with stacks of data, apply fusion networks and by generating pre-trained networks for the available data of different sensors.

This record has no associated files available for download.

Contributors

Author: Jonathon Hare

Download statistics