loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Amirmasoud Ghiassi 1 ; Robert Birke 2 and Lydia Y. Chen 1

Affiliations: 1 Delft University of Technology, Delft, The Netherlands ; 2 ABB Research, Baden-Dättwil, Switzerland

Keyword(s): Crowdsourcing, Deep Learning, Noisy Labels, Label Aggregation.

Abstract: Today, to label the massive datasets needed to train Deep Neural Networks (DNNs), cheap and error-prone methods such as crowdsourcing are used. Label aggregation methods aim to infer the true labels from noisy labels annotated by crowdsourcing workers via labels statistics features. Aggregated labels are the main data source to train deep neural networks, and their accuracy directly affects the deep neural network performance. In this paper, we argue that training DNN and aggregating labels are not two separate tasks. Incorporation between DNN training and label aggregation connects data features, noisy labels, and aggregated labels. Since each image contains valuable knowledge about its label, the data features help aggregation methods enhance their performance. We propose LABNET an iterative two-step method. Step one: the label aggregation algorithm provides labels to train the DNN. Step two: the DNN shares a representation of the data features with the label aggregation algorithm. These steps are repeated until the converging label aggregation error rate. To evaluate LABNET we conduct an extensive empirical comparison on CIFAR-10 and CIFAR-100 under different noise and worker statistics. Our evaluation results show that LABNET achieves the highest mean accuracy with an increase of at least 8% to 0.6% and lowest error rate with a reduction of 7.5% to 0.25% against existing aggregation and training methods in most cases. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.239.76.25

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Ghiassi, A.; Birke, R. and Chen, L. (2022). LABNET: A Collaborative Method for DNN Training and Label Aggregation. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 56-66. DOI: 10.5220/0010770400003116

@conference{icaart22,
author={Amirmasoud Ghiassi. and Robert Birke. and Lydia Y. Chen.},
title={LABNET: A Collaborative Method for DNN Training and Label Aggregation},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2022},
pages={56-66},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010770400003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - LABNET: A Collaborative Method for DNN Training and Label Aggregation
SN - 978-989-758-547-0
IS - 2184-433X
AU - Ghiassi, A.
AU - Birke, R.
AU - Chen, L.
PY - 2022
SP - 56
EP - 66
DO - 10.5220/0010770400003116
PB - SciTePress