Summary

In a few specific cases, convolutional neural network architectures trained on images allow us to reuse learned features in a new network. The performance benefits of transferring features decrease the more dissimilar the base task and target task are. It is surprising to know that initializing a convolutional neural network with transferred features from almost any number of layers can produce a boost to generalization performance after fine-tuning to a new dataset.