Demo Data from VLDB 2026 / reviewers in the wild / expert
Donghyun Na
dblp:241/9784
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Rendering · 100% | |
| Artificial intelligence
2 papers |
Trustworthy machine learning · 44% Transfer learning and domain adaptation · 22% Probabilistic and Bayesian machine learning · 22% |
Topics — the 9 heaviest of 10, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Rendering
physically based rendering |
0.8 | 1 | 2024 | SwitchLight: Co-Design of Physics-Driven Architecture and Pre-training Framework for Human Portrait Relighting · CVPR 2024 |
Rendering › relighting
portrait relighting |
0.8 | 1 | 2024 | SwitchLight: Co-Design of Physics-Driven Architecture and Pre-training Framework for Human Portrait Relighting · CVPR 2024 |
Rendering
reflectance modeling |
0.8 | 1 | 2024 | SwitchLight: Co-Design of Physics-Driven Architecture and Pre-training Framework for Human Portrait Relighting · CVPR 2024 |
Rendering
relighting |
0.8 | 1 | 2024 | SwitchLight: Co-Design of Physics-Driven Architecture and Pre-training Framework for Human Portrait Relighting · CVPR 2024 |
Machine learning › Probabilistic and Bayesian machine learning
bayesian meta-learning |
0.4 | 1 | 2020 | Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks · ICLR 2020 |
Machine learning › Transfer learning and domain adaptation
meta-learning |
0.4 | 1 | 2020 | Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks · ICLR 2020 |
Machine learning › Trustworthy machine learning
out-of-distribution generalization |
0.4 | 1 | 2020 | Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks · ICLR 2020 |
Machine learning › Trustworthy machine learning
robustness |
0.4 | 1 | 2020 | Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks · ICLR 2020 |
Machine learning › Representation and self-supervised learning › representation learning › unsupervised representation learning
self-supervised representation learning |
0.2 | 1 | 2024 | SwitchLight: Co-Design of Physics-Driven Architecture and Pre-training Framework for Human Portrait Relighting · CVPR 2024 |
Methods — techniques the papers use, named apart from their topics
cook-torrance reflectance model · 1.5self-supervised pretraining · 0.8self-supervised pre-training · 0.8task balancing · 0.4bayesian meta-learning · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | SwitchLight: Co-Design of Physics-Driven Architecture and Pre-training Framework for Human Portrait RelightingabstractWe introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pretraining framework. Drawing on the Cook-Torrance reflectance model, we have meticulously configured the architecture design to precisely simulate light-surface interactions. Furthermore, to overcome the limitation of scarce high-quality lightstage data, we have developed a self-supervised pretraining strategy. This novel combination of accurate physical modeling and expanded training dataset establishes a new benchmark in relighting realism. Minje Jang, Wonjun Yoon, Jisoo Lee, Donghyun Na, Sanghyun Woo |
CVPR | 5 |
| 2020 | Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks
Haebeom Lee, Hayeon Lee, Donghyun Na, Saehoon Kim, Minseop Park, Eunho Yang, Sung Ju Hwang |
ICLR | 3 |