Reviews: Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks

The maximal correlations and correlation functions are then used to predict the class for the target sample. The evaluation is done on 3 datasets (CIFAR100, Stanford Dogs, and Tiny Imagenet). The proposed MCW method is compared with SVM trained on output of the penultimate layer. For all the datasets, the Multi-Source MCW shows significant advantage especially when there are few samples.