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.

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