1 code implementation • 17 Apr 2024 • Yeonguk Yu, Sungho Shin, Seunghyeok Back, Minhwan Ko, Sangjun Noh, Kyoobin Lee
After blocks are adjusted for current test domain, we generate pseudo-labels by averaging given test images and corresponding flipped counterparts.
1 code implementation • 8 Mar 2023 • Sungho Shin, Yeonguk Yu, Kyoobin Lee
This approach differs from conventional knowledge distillation frameworks, which use the L_p distance metrics and offer the advantage of converging well when reducing the distance between features of different resolutions.
1 code implementation • CVPR 2023 • Yeonguk Yu, Sungho Shin, Seongju Lee, Changhyun Jun, Kyoobin Lee
In this study, we first revealed that a norm of the feature map obtained from the other block than the last block can be a better indicator of OOD detection.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
1 code implementation • 29 Sep 2022 • Sungho Shin, Joosoon Lee, Junseok Lee, Yeonguk Yu, Kyoobin Lee
Deep learning has achieved outstanding performance for face recognition benchmarks, but performance reduces significantly for low resolution (LR) images.
1 code implementation • 20 Sep 2022 • Seongju Lee, Yeonguk Yu, Seunghyeok Back, Hogeon Seo, Kyoobin Lee
Conventionally, learning-based automatic sleep scoring on single-channel electroencephalogram (EEG) is actively studied because obtaining multi-channel signals during sleep is difficult.
no code implementations • 22 Aug 2020 • Jongwon Kim, Sungho Shin, Yeonguk Yu, Junseok Lee, Kyoobin Lee
We divided a single deep learning architecture into a common extractor, a cloud model and a local classifier for the distributed learning.