no code implementations • 7 Sep 2022 • Jingcong Li, Fei Wang, Haiyun Huang, Feifei Qi, JiaHui Pan
The proposed SSML learns a meta model with the existing subjects first, then fine-tunes the model in a semi-supervised learning manner, i. e. using few labeled and many unlabeled samples of target subject for calibration.