Visualization of the Feature Space of Neural Networks

Abstract

Visualization of a learning machine can be crucial to understand its behaviour, specially in the case of (deep) neural networks, since they are quite difficult to interpret. An approach for visualizing the feature space of a neural network is presented, trying to answer to the question “what representation of the data is the network using to make its decision?” The proposed method gives a representation of the space where the network is tackling the problem, reducing it while respecting the linearity of the model. As shown experimentally, this technique allows to study the evolution of the model with respect to the training epochs, to have a representation of the data similar to the one used by the neural network, and even to detect groups of patterns that behave differently