Transfer learning in ML refers to the general idea of taking a model trained for one domain and applying it to another. However this article seems to only focus on feature mapping, ie. breaking down images into features using hidden layers of ImageNet models. In this case, the pretrained model is only acting as a feature extractor because it is not trained to maximize the embedded distance between the classes you are trying to differentiate.