Exploiting Synthetic Data for Data Imbalance Problems: Baselines from a Data Perspective
ICML Workshop on MMFM: What is Next in Multimodal Foundation Models? (MMFM) 2023
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Nayeong Kim
I am a Ph.D candidate in computer science and engineering department at POSTECH, advised by Suha Kwak. My long-term research goal is to work 'Toward ensuring the trustworthiness of AI models.' During my pursuit of a Ph.D., I aim to address the pressing issue of combating spurious correlations that undermine the integrity of AI models in real-world applications. I am a member of Computer Vision Lab at POSTECH. Previously, I received a B.S. in computer science and engineering department from POSTECH in 2017.
Publications
Removing Multiple Biases through the Lens of Multi-task Learning
ICML Workshop on Spurious Correlations, Invariance and Stability (SCIS) 2023
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Learning Debiased Classifier with Biased Committee
ICML Workshop on Spurious Correlations, Invariance and Stability (SCIS) 2022
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Awards
- Qualcomm Innovation Fellowship Winner , Qualcomm Korea Corp., “Leveraging Proxy of Training Data for Test-Time Adaptation” (2023)
- Samsung Strategy and Innovation Paper Award, “Leveraging Proxy of Training Data for Test-Time Adaptation” (2023)
- BK21 Best Paper Award from POSTECH CSE, “Learning Debiased Classifier with Biased Committee” (2023)
Talks
- “Learning Debiased Classifier with Biased Committee”, Korean Artificial Intelligence Association Fall conference, NAVER and KAIA, Nov. 2022
Services
- Reviewer in ECCV(2024), ACCV(2024), CVPR(2024), ICLR (2024), AAAI (2024), WACV (2024), NeurIPS (2023), ICCV (2023), Tiny Papers@ICLR (2023, 2024), ICML (2022)