CrossSiam: k-Fold Cross Representation Learning

Kaiyu Suzuki, Yasushi Kambayashi, Tomofumi Matsuzawa

2022

Abstract

One of the most important tasks for multi-agents such as drones is to automatically make decisions based on images captured by on-board cameras. These agents must be highly accurate and reliable. For this purpose, we applied k-fold cross validation to the task of classifying images using deep learning, which is a method that compares and evaluates models appropriately model of a given problem; this technique is easy to understand and easy to implement, and it produces results in lower bias estimates. However, k-fold cross validation reduces the amount of data per neural network, which reduces the accuracy. In order to address this problem, we propose CrossSiam. CrossSiam is a one of the representation learning methods to train feature encoders to mimic the embedding space of the validation data of each neural network. We show that the proposed method has a higher classification accuracy than the ParaSiam (baseline). This approach can be very important in the field where reliability is required, such as automated vehicles and drones in disaster situations.

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Paper Citation


in Harvard Style

Suzuki K., Kambayashi Y. and Matsuzawa T. (2022). CrossSiam: k-Fold Cross Representation Learning. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: SDMIS, ISBN 978-989-758-547-0, pages 541-547. DOI: 10.5220/0010972500003116


in Bibtex Style

@conference{sdmis22,
author={Kaiyu Suzuki and Yasushi Kambayashi and Tomofumi Matsuzawa},
title={CrossSiam: k-Fold Cross Representation Learning},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: SDMIS,},
year={2022},
pages={541-547},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010972500003116},
isbn={978-989-758-547-0},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: SDMIS,
TI - CrossSiam: k-Fold Cross Representation Learning
SN - 978-989-758-547-0
AU - Suzuki K.
AU - Kambayashi Y.
AU - Matsuzawa T.
PY - 2022
SP - 541
EP - 547
DO - 10.5220/0010972500003116