Tackling Data Bias in Painting Classification with Style Transfer

Mridula Vijendran, Frederick Li, Hubert P. H. Shum

2023

Abstract

It is difficult to train classifiers on paintings collections due to model bias from domain gaps and data bias from the uneven distribution of artistic styles. Previous techniques like data distillation, traditional data augmentation and style transfer improve classifier training using task specific training datasets or domain adaptation. We propose a system to handle data bias in small paintings datasets like the Kaokore dataset while simultaneously accounting for domain adaptation in fine-tuning a model trained on real world images. Our system consists of two stages which are style transfer and classification. In the style transfer stage, we generate the stylized training samples per class with uniformly sampled content and style images and train the style transformation network per domain. In the classification stage, we can interpret the effectiveness of the style and content layers at the attention layers when training on the original training dataset and the stylized images. We can tradeoff the model performance and convergence by dynamically varying the proportion of augmented samples in the majority and minority classes. We achieve comparable results to the SOTA with fewer training epochs and a classifier with fewer training parameters.

Paper Citation

in Harvard Style

Vijendran M., Li F. and Shum H. (2023). Tackling Data Bias in Painting Classification with Style Transfer. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 250-261. DOI: 10.5220/0011776600003417

in Bibtex Style

@conference{visapp23,
author={Mridula Vijendran and Frederick Li and Hubert P. H. Shum},
title={Tackling Data Bias in Painting Classification with Style Transfer},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={250-261},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011776600003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - Tackling Data Bias in Painting Classification with Style Transfer
SN - 978-989-758-634-7
AU - Vijendran M.
AU - Li F.
AU - Shum H.
PY - 2023
SP - 250
EP - 261
DO - 10.5220/0011776600003417
PB - SciTePress