46 citations · 52 across the 4 of their papers we have counts for
4 papers
Subject-Adaptive Transfer Learning Using Resting State EEG Signals for Cross-Subject EEG Motor Imagery Classification
Sion An, Myeongkyun Kang, Soopil Kim +3
Electroencephalography (EEG) motor imagery (MI) classification is a fundamental, yet challenging task due to the variation of signals between individuals i.e., inter-subject variab…
CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification
Philip Chikontwe, Soopil Kim, Sang Hyun Park
Few-shot classification is a challenging problem that aims to learn a model that can adapt to unseen classes given a few labeled samples. Recent approaches pre-train a feature extr…
Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation
Soopil Kim, Sion An, Philip Chikontwe +1
Segmentation of organs of interest in 3D medical images is necessary for accurate diagnosis and longitudinal studies. Though recent advances using deep learning have shown success…
Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification
Sion An, Soopil Kim, Philip Chikontwe +1
Brain-Computer Interfaces (BCI) based on Electroencephalography (EEG) signals, in particular motor imagery (MI) data have received a lot of attention and show the potential towards…