LAIT는 인간의 인식을 넘어, 우리가 보고 느끼는 세상을 더욱 정교하게 모델링하고 이해하는 것을 목표로 합니다. 그리고 생성 모델이 이를 위한 가장 강력한 해결책이자 효과적인 접근법이라고 믿습니다. 이 때, 단순히 높은 성능의 모델을 목표로 하기보다는, 설명 가능하고, 효율적이며, 직관적이고, 사용하기 쉬운 이미징 알고리즘 개발을 추구합니다. 이를 통해 세상을 경험하고 해석하는 방식에 혁신적인 변화를 일으키고자 합니다.

Our mission is to redefine the boundaries of what can be seen and understood.

• Together, we go beyond perception, modeling the world we see and sense.
• We challenge the limits by developing intelligent models that reveal structures of the world beyond our senses.
• Generative models, we believe, are the most powerful—and indeed the only—solution for building systems that understand the world.
• Grounded in simple, clear principles, we aim to create technology that is both intuitive and easy to use—solutions that transform how we experience and interpret our world.

By combining the rigor of signal processing with the creativity of machine learning, we transform imaginations into reality. We build algorithms that analyze and synthesize images, audios, and videos to construct comprehensive “world models” of the seen and the sensed. Along the way, our models uncover mathematical insights and reveal the beauty of hidden patterns in nature, pushing the frontier of signal modeling and interpretation.

[Wanted] I am looking for strong and motivated students (or interns). The partial list of future project topics are as follows:

• Computational Imaging (e.g., super-resolution, image compression, medical image reconstruction, etc.)
• Generative models for High-Dimensional Data (beyond 2D)
• Better Conditioning, Better Generation
• Explainable & Reliable Generation
• Understanding Intelligent Models & Representations

For more details, please check Research Questions. Students who are interested in joining our lab, please check Call for Students & Interns.

Selected Talks

Image Enhancement Techniques: CutBlur, WCT2, and SimUSR CVPR 2020 (Naver LABS) — video

신호처리 이론으로 실용적인 스타일 변환 모델 만들기 (Better Faster Stronger Transfer) Deview 2019

READ A LOT, THINK IN PICTURES, CODE IT, VISUALIZE MORE!