Transfer learning – what it is, and when to use it

Transfer learning is a deep learning strategy that reuses knowledge gained from solving one problem by applying it to a different, but related, problem. For example, let's say we have three types of flowers, namely, a rose, a sunflower, and a tulip. We can use the standard pre-trained models, such as VGG16/19, ResNet50, or InceptionV3 models (pre-trained on ImageNet with 1000 output classes, which can be found at https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) to classify the flower images, but our model wouldn't be able to correctly identify them because these flower categories were not learned by the models. In other words, they are classes that the model is not aware of.

The following image shows how the flower images are classified wrongly by the pre-trained VGG16 model (the code is left to the reader as an exercise):

Transfer learning with Keras

Training of pre-trained models is done on many comprehensive image classification problems...