Alumni
Subeen Lee
MS student, 2023.09 – 2025.09
- BS from KAIST, 2023 (Summa Cum Laude; Major in Chemical & Biomolecular Engineering; Minor in Computer Science)
3 notes
Research
2025
Diverse Rare Image Generation
Latent optimization and feature-space density estimation generate rare images with controllable rarity, diversity, and reference similarity.
2025
Diverse Rare Sample Generation with Pretrained GANs
This study proposes a novel approach for generating diverse rare samples from high-resolution image datasets with pretrained GANs. Our method employs gradient-based optimization of latent vectors within a multi-objective framework and utilizes normalizing flows for density estimation on the feature space. This enables the generation of diverse rare images, with controllable parameters for rarity, diversity, and similarity to a reference image. We demonstrate the effectiveness of our approach both qualitatively and quantitatively across various datasets and GANs without retraining or fine-tuning the pretrained GANs.
2023
Explainable AI-Based Interface System for Weather Forecasting Model
As machine learning (ML) becomes more prevalent in meteorological decision-making, user-centered explainable artificial intelligence (XAI) studies have yet to extend into this domain. This study identifies three key requirements for explaining black-box models in meteorology: model bias in different rainfall scenarios, model reasoning, and output confidence. Appropriate XAI methods are mapped to these requirements and evaluated both quantitatively and qualitatively. The results show that intuitive explanations improve decision-making utility and user trust, highlighting the need for user-centered XAI to enhance the usability of AI systems in practice.
4 papers