Feature Alignment as a Generative Process

Feature visualization Olah et al. (2017) is a set of techniques for neural networks aiming to find inputs that maximize the activation of one or more selected neurons from the same network. Usually, feature visualization is used as a method for model interpretability, where one seeks to understand a neural network by analyzing how much each neuron contributes to a neural network by perceiving the images generated by these techniques. The process of obtaining these inputs is, in a sense, an attempt towards reversing a neural network. Since a neural network is composed by functions that map inputs to outputs, the visual representation of a feature is the input we would have given a target activation for a group of posterior selected neurons. The reversibility of neural networks relates to how well one can reverse the map from the activation of target neurons back to the input neurons Gomez et al. (2017). In most cases, neural networks are not reversible, primarily due to three reasons: (1) the presence of non-reversible activation functions (e.g., ReLU Nair and Hinton (2010)), which means that in general, it is impossible to directly recover the input value x given the output value f(x).