no code implementations • 16 Nov 2020 • Ruqian Hao, Khashayar Namdar, Lin Liu, Farzad Khalvati
The model achieved AUC of 82% compared with AUC of 78. 48% for the baseline, which reassures the robustness and stability of our proposed transfer learning augmented with active learning framework while significantly reducing the size of training data.
no code implementations • 1 Jun 2020 • Ruqian Hao, Khashayar Namdar, Lin Liu, Masoom A. Haider, Farzad Khalvati
Data augmentation refers to a group of techniques whose goal is to battle limited amount of available data to improve model generalization and push sample distribution toward the true distribution.