Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective
Song Liu, Kenji Fukumizu · 2016
Transfer learning assumes classifiers of similar tasks share certain parameter structures. Unfortunately, modern classifiers use sophisticated feature representations with huge parameter spaces which lead to costly transfer. Under the impression that changes from one classifier to another should be “simple”, an efficient transfer learning criterion that only learns the “differences” is proposed in this paper. We train a posterior ratio which turns out to minimize the upper-bound of the target learning risk. The model of posterior ratio does not have to share the same parameter space with the source classifier at all so it can be easily modelled and efficiently trained. The resulting classifier therefore is obtained by simply multiplying the existing probabilistic-classifier with the learned posterior ratio.