Transfer learning for ensembles: reducing computation time and keeping the diversity

no code implementations • 27 Jun 2022 • Ilya Shashkov, Nikita Balabin, Evgeny Burnaev, Alexey Zaytsev

Our approach for the transfer learning of ensembles consists of two steps: (a) shifting weights of encoders of all models in the ensemble by a single shift vector and (b) doing a tiny fine-tuning for each individual model afterwards.

Transfer Learning