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

Tackling the small data problem in deep learning with multi-sensor approaches
Tackling the small data problem in deep learning with multi-sensor approaches
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.
Deep Learning, Multi-Sensor data, Transfer Learning
Kramer, Iris, Caroline
ba56efc0-ce81-4897-9d74-4bec3e9e1fca
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Kramer, Iris, Caroline
ba56efc0-ce81-4897-9d74-4bec3e9e1fca
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e

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)

Record type: Conference or Workshop Item (Other)

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.

Full text not available from this repository.

More information

Accepted/In Press date: 8 December 2017
Venue - Dates: 46th Computer Applications and Quantitative Methods in Archaeology: Human History and Digital Future, Tübingen, Germany, 2018-03-19 - 2018-03-23
Keywords: Deep Learning, Multi-Sensor data, Transfer Learning

Identifiers

Local EPrints ID: 416399
URI: http://eprints.soton.ac.uk/id/eprint/416399
PURE UUID: eacf16cb-14c2-4087-a16a-0071aea77775
ORCID for Jonathon Hare: ORCID iD orcid.org/0000-0003-2921-4283

Catalogue record

Date deposited: 15 Dec 2017 17:30
Last modified: 14 Mar 2019 01:41

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Contributors

Author: Iris, Caroline Kramer
Author: Jonathon Hare ORCID iD
Author: Adam Prugel-Bennett

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