Transfer learning for ensembles: reducing computation time and keeping the diversity
Ilya Shashkov, Alexey Zaytsev, Nikita Balabin, Evgeny Vladimirovich Burnaev · 2022
Transferring a deep neural network trained on one problem to another requires only a small amount of data and little additional computation time. The same behaviour holds for ensembles of deep learning models typically superior to a single model. However, a transfer of deep neural networks ensemble demands relatively high computational expenses. The probability of overfitting also increases.