Tackling Data Bias in Painting Classification with Style Transfer (2023)
Conference Proceeding
Vijendran, M., Li, F. W., & Shum, H. P. (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 - Volume 5 VISAPP: VISAPP (250-261). https://doi.org/10.5220/0011776600003417

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 transf... Read More about Tackling Data Bias in Painting Classification with Style Transfer.

Gamifying Experiential Learning Theory (2023)
Conference Proceeding
Alsaqqaf, A., & Li, F. W. (in press). Gamifying Experiential Learning Theory. In ICWL 2022, SETE 2022: Learning Technologies and Systems (16-28). https://doi.org/10.1007/978-3-031-33023-0_2