Analyzing Effects of Mixed Sample Data Augmentation on Model Interpretability. - Soyoun Won; Sung-Ho Bae; Seong Tae Kim 001 (CoRR, 2023)

Dissecting Mixed-Sample Data Augmentation Models via Neural-concept Association. - Soyoun Won; Sung-Ho Bae; Seong Tae Kim 001 (ICOIN, 2024)

PRISM: Pseudo-Labeling and Region-Based Inpainting for Synthetic Change Detection Modeling. - Enki Cho; Soyoun Won; Sung-Sik Choo; Seong Tae Kim 001 (IEEE Geosci. Remote. Sens. Lett., 2025)

Effects of mixed sample data augmentation on interpretability of neural networks. - Soyoun Won; Sung-Ho Bae; Seong Tae Kim 001 (Neural Networks, 2025)