GAP: Quantifying the Generative Adversarial Set and Class Feature Applicability of Deep Neural Networks

Abstract

Recent work in deep neural networks has sought to characterize the nature in which a network learns features and how applicable learnt features are to various problem sets. Deep neural network applicability can be split into three sub-problems; set applicability, class applicability, and instance applicability. In this work we seek to quantify the applicability of features learned during adversarial training, focusing specifically on set and class applicability. We apply techniques for measuring applicability to both generators and discriminators trained on various data sets to quantify applicability.