Machine Learning / Deep Learning
Understand patterns from large-scale data.
In the field of machine learning and deep learning, technology that contributes to practical use has been born due to the recent development of computer technology and communication technology.
Now, machine learning technology is expected to solve many important issues in the real world.
The issues that we need to solve are extremely complex and diverse, including abnormality detection, automatic operation, plant and robot control, and drug discovery.
To solve these issues, PFN gathers experts from a wide range of fields around machine learning, from basic theory to actual application, and works together to conduct research.
The results obtained there will be returned in the form of paper presentations, open source library publications, and application to real systems, and will respond widely to the demands of society.
Publications
Decomposing NeRF for Editing via Feature Field Distillation
NeurIPS 2022
By : Sosuke Kobayashi, Eiichi Matsumoto, Vincent Sitzmann
Interactive Hyperparameter Optimization with Paintable Timelines
DIS 2021
By : Keita Higuchi, Shotaro Sano, Takeo Igarashi
Warp-Refine Propagation: Semi-Supervised Auto-labeling via Cycle-consistency
ICCV 2021
By : Aditya Ganeshan, Alexis Vallet, Yasunori Kudo, Shin-ichi Maeda, Tommi Kerola, Rares Ambrus, Dennis Park, Adrien Gaidon
Hierarchical Lovász Embeddings for Proposal-free Panoptic Segmentation
CVPR 2021
By : Tommi Kerola, Jie Li, Atsushi Kanehira, Yasunori Kudo, Alexis Vallet, Adrien Gaidon