Towards a Better Understanding of Deep Neural Networks Representations using Deep Generative Networks
Jérémie Despraz, Stéphane Gomez, Héctor F. Satizábal, Carlos Andrés Peña-Reyes · 2017
This paper presents a novel approach to deep-dream-like image generation for convolutional neural networks (CNNs).Images are produced by a deep generative network from a smaller dimensional feature vector.This method allows for the generation of more realistic looking images than traditional activation-maximization methods and gives insight into the CNN's internal representations.Training is achieved by standard backpropagation algorithms.