11
Mar
![Teaser image to Enlarging the Capability of Diffusion Models for Inverse Problems by Guidance](/images/events/2024-03-11-munich-ai-lectures-ye.webp)
Enlarging the Capability of Diffusion Models for Inverse Problems by Guidance
Jong Chul Ye, Graduate School of Artificial Intelligence, KAIST, Korea
11.03.2024
6:15 pm - 7:45 pm
LMU Munich, Theresienstraße 39, Room B 006
The talk introduces two key innovations for enhancing diffusion models in solving inverse problems:
First, a method leveraging two perpendicular 2D diffusion models to tackle 3D problems by representing 3D data as intersecting 2D slices, addressing dimensionality challenges.
Second, a novel solver that uses text prompts to clarify ambiguities, inspired by human perception, dynamically incorporating textual guidance for more accurate solutions.
Experimental results confirm these approaches significantly improve problem-solving accuracy and reduce ambiguities.
Organized by:
baiosphere
Bavarian Academy of Science and Humanities
Helmholtz Munich
LMU Munich
TUM
AI-HUB LMU
ELLIS Munich Unit
Konrad Zuse School of Excellence in Reliable AI
MCML
Munich Data Science Institute TUM
Munich Institute of Robotics and Machine Intelligence TUM
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