The purpose of feature extraction on convolutional neural networks is to reuse deep representations learnt for a pre-trained model to solve a new, potentially unrelated problem.
WordNet, which includes a wide variety of concepts associated with words (i. e., synsets), is often used as a source for computing those distances.
In this paper we evaluate the impact of using the Full-Network embedding in this setting, replacing the original image representation in a competitive multimodal embedding generation scheme.
Patterns stored within pre-trained deep neural networks compose large and powerful descriptive languages that can be used for many different purposes.
Transfer learning for feature extraction can be used to exploit deep representations in contexts where there is very few training data, where there are limited computational resources, or when tuning the hyper-parameters needed for training is not an option.